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| Property | Value |
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| URL | https://en.wikipedia.org/wiki/Graph_database |
| Last Crawled | 2026-04-09 19:19:56 (2 days ago) |
| First Indexed | 2013-08-15 11:13:45 (12 years ago) |
| HTTP Status Code | 200 |
| Meta Title | Graph database - Wikipedia |
| Meta Description | null |
| Meta Canonical | null |
| Boilerpipe Text | A
graph database
(
GDB
) is a
database
that uses
graph structures
for
semantic queries
with
nodes
,
edges
, and properties to represent and store data.
[
1
]
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.
[
2
]
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
[
3
]
and lack easy
traversal
over a chain of edges.
[
4
]
The underlying storage mechanism of graph databases can vary. Relationships are first-class citizens in a graph database and can be labeled, directed, and given properties. 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 a level of abstraction between the graph database management system and physical storage devices). Others use a
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
. There is a wide variety of systems used, many of which are tightly tied to one product. Early standardization efforts led 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)
[
5
]
.
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.
[
6
]
Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations using proprietary graph databases,
[
7
]
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.
[
8
]
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.
[
9
]
[
10
]
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.
[
11
]
[
12
]
Commercial
object databases
(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.
[
citation needed
]
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
scaled horizontally
became available. Further,
SAP HANA
brought
in-memory
and
columnar
technologies to graph databases.
[
13
]
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. Also during the decade,
cloud
-based graph databases such as
Amazon Neptune
and
Neo4j AuraDB
became available.
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
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-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.
Labeled-property graph
[
edit
]
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
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;
[
14
]
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.
[
15
]
Direct storage of relationships allows a
constant-time
traversal
.
[
16
]
Resource Description Framework (RDF)
[
edit
]
An example RDF graph
In an
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
hasName
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
and/or be identified by a
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
URI
.
[
17
]
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.
[
citation needed
]
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. 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
[
edit
]
Data lookup performance is dependent on the access speed from one particular node to another. Because
index
-free adjacency enforces the nodes to have direct physical
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
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 traversals
. Native graph databases use index-free adjacency to process
CRUD
operations on the stored data.
Multiple categories of graphs by kind of data have been recognised. Gartner suggests the five broad categories of graphs:
[
18
]
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
(IoT) devices.
Comparison with relational databases
[
edit
]
Since
Edgar F. Codd
's 1970 paper on the
relational model
,
[
19
]
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 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.
[
19
]
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 databases
, graph databases are often faster for associative data sets
[
20
]
and map more directly to the structure of
object-oriented
applications. They can scale more naturally
[
21
]
to large datasets as they do not typically need
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
[
22
]
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.
[
23
]
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
indexes
, 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.
[
24
]
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 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.
[
24
]
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 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.
[
24
]
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
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. In terms of
big O notation
, this query would be
time – i.e., proportional to the logarithm of the size of the data. In contrast, the relational version would be multiple
lookups, plus the
time needed to join all of the data records.
[
24
]
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.
[
25
]
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.
[
25
]
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
people
table (which has a
person_id
and
person_name
column) and a
friend
table (with
friend_id
and
person_id
, which is a
foreign key
from the
people
table). In this case, searching for all of Jack's friends would result in the following SQL query.
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'
;
The same query may be translated into --
Cypher
, a graph database
query language
MATCH
(
p1
:
person
{
name
:
'Jack'
})
-[
:
FRIEND_WITH
]-
(
p2
:
person
)
RETURN
p2
.
name
Gremlin
, an imperative style graph
query language
maintained by Apache TinkerPop and used by many graph databases
[
26
]
g
.
V
().
hasLabel
(
"person"
).
has
(
"name"
,
"Jack"
).
out
(
"friendsWith"
).
hasLabel
(
"person"
).
values
(
"name"
)
SPARQL
, an RDF graph database
query language
standardized by
W3C
and used in multiple RDF
Triple
and
Quad
stores
Long form
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
.
}
Short form
PREFIX
foaf
:
<http://xmlns.com/foaf/0.1/>
SELECT
?name
WHERE
{
?s
foaf
:
name
"Jack"
;
foaf
:
knows
?o
.
?o
foaf
:
name
?name
.
}
SPASQL, a hybrid database query language, that extends
SQL
with
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
;
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.
[
16
]
List of graph databases
[
edit
]
The following is a list of
notable
graph databases:
Name
Current
version
Latest
release
date
(YYYY-MM-DD)
Software
license
Programming language
Description
Aerospike
7.0
2024-05-15
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
[
27
]
2.16.0
2025-09-12
[
28
]
Apache 2
Community version,
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
2022-12-20
Proprietary
, clients:
Eclipse Public License
v1
C#
,
C
,
Common Lisp
,
Java
,
Python
Resource Description Framework
(RDF) and graph database.
Amazon
Neptune
1.4.7.0
2026-03-03
[
29
]
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
RDF
, and their respective
query languages
Apache TinkerPop,
Gremlin
,
SPARQL
, and
openCypher
.
Altair Graph Studio
6.3
2025-12
Proprietary
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.
[
30
]
[
31
]
[
32
]
[
33
]
ArangoDB
3.12.4.2
2025-04-09
Free
Apache 2
,
Proprietary
C++
,
JavaScript
,
.NET
,
Java
,
Python
,
Node.js
,
PHP
,
Scala
,
Go
,
Ruby
,
Elixir
NoSQL
native graph database system developed by ArangoDB Inc, supporting three data models (key/value, documents, graphs, vector), 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
Apache Gremlin
query language
DataStax
Enterprise
Graph
v6.0.1
2018-06
Proprietary
Java
Distributed, real-time, scalable database; supports Tinkerpop, and integrates with
Cassandra
[
34
]
FalkorDB
4.16
2026-01
SSPLv1
C
,
Rust
High-performance, in-memory graph database that uses sparse matrix multiplication (
GraphBLAS
) for query execution; it is the community-led successor to
RedisGraph
following its end-of-life; optimized for low-latency
GraphRAG
and AI applications.
Graph in Microsoft Fabric
2025-10
Proprietary
Not disclosed
Microsoft's first native, horizontally scalable graph data platform that integrates data management, analytics, and visualization. It uses
GQL
as the query language.
Google Spanner Graph
N/A
2025-01
Proprietary
C++
A horizontally scalable, multi-model database that provides a native graph experience on top of
Spanner
; supports the
ISO
GQL
standard and
openCypher
for matching patterns and traversing relationships; designed for global consistency and trillions of edges.
GUN (Graph Universe Node)
0.2020.1240
2024
Open source,
MIT License
,
Apache 2.0
,
zlib License
JavaScript
An
open source
,
offline-first
,
real-time
,
decentralized
, graph database written in
JavaScript
for the
web browser
.
[
35
]
[
36
]
It is implemented as a
peer-to-peer
network featuring
multi-master replication
with a custom
commutative replicated data type (CRDT)
.
[
citation needed
]
InfiniteGraph
2021.2
2021-05
Proprietary
,
commercial, free 50GB version
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
1.1.0
2024-11-07
[
37
]
Apache 2
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
);
[
38
]
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
).
[
39
]
Kùzu
0.11.3
2025-10
MIT
C++
Embeddable, open-source graph database management system (GDBMS) featuring a vectorized query engine; the project was abandoned by its creator and sponsor Kùzu Inc. in October 2025, with the repository archived and documentation moved to GitHub.
[
40
]
Ladybug
0.15.1
2026-03
MIT
C++
Embedded columnar graph database, based on a fork of Kùzu. Supports querying Apache Parquet and Apache Arrow with zero copy.
[
41
]
MarkLogic
8.0.4
2015
Proprietary
,
freeware
developer version
Java
Multi-model
NoSQL
database that stores
documents
(JSON and XML) and semantic graph data (
RDF
triples); also has a built-in search engine.
Microsoft SQL Server
2017
RC1
Proprietary
SQL
/T-SQL,
R
,
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.
[
42
]
NebulaGraph
3.8.0
2024-05
Open Source Edition is under Apache 2.0, Common Clause 1.0
C++
,
Go
,
Java
,
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.
[
43
]
Neo4j
2026.03.1
2026-04-02
[
44
]
GPLv3
Community Edition,
commercial
and
AGPLv3
options for enterprise and advanced editions
Java
,
.NET
,
JavaScript
,
Python
,
Go
,
Ruby
,
PHP
,
R
,
Erlang
/
Elixir
,
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
REST
web API
interface, and a proprietary Bolt protocol with official drivers.
Ontotext GraphDB
11.3.1
2026-02-19
[
45
]
Proprietary
, Standard and Enterprise Editions are
commercial
, Free Edition is
freeware
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
Lucene
,
SOLR
and
Elasticsearch
for
Full text
and
Faceted search
, and
Kafka
for event and stream processing. Supports
OGC
GeoSPARQL
. Provides
JDBC
access to
Knowledge Graphs
.
[
46
]
OpenLink
Virtuoso
8.2
2018-10
Open Source Edition is
GPLv2
, Enterprise Edition is
proprietary
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
Semantic Desktop), a one-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server
[
47
]
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
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
Community Edition is
Apache 2
, Enterprise Edition is
commercial
Java
Second-generation
[
48
]
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
REST
and
JSON
API
.
RedisGraph
2.0.20
2020-09
Redis Source Available License,
AGPLv3
,
SSPL
C
In-memory, queryable Property Graph database which uses
sparse matrices
to represent the
adjacency matrix
in graphs and
linear algebra
to query the graph.
[
49
]
SAP HANA
2.0 SPS 05
2020-06
[
50
]
Proprietary
C
,
C++
,
Java
,
JavaScript
and
SQL
-like language
In-memory
ACID
transaction supported property graph
[
51
]
Sparksee
5.2.0
2015
Proprietary
,
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
, C++,
C#
,
Python
, and
Objective-C
; version 5 is the first graph
mobile database
.
Teradata
Aster
7
2016
Proprietary
Java
,
SQL
,
Python
,
C++
,
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
[
52
]
TerminusDB
11.0.6
2023-05-03
[
53
]
Apache 2
Prolog
,
Rust
,
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
[
54
]
Proprietary
C++
Massive parallel processing
(MPP) native graph database management system
[
55
]
TinkerGraph
3.8.0
2025-11-12
[
56
]
Apache 2
Java
TinkerGraph is a single machine, in-memory (with optional persistence), graph engine that provides both OLTP and OLAP functionality. TinkerGraph is deployed with Apache TinkerPop and serves as the reference implementation for other providers to study in order to understand the semantics of the various methods of the TinkerPop API.
[
57
]
TypeDB
2.14.0
2022-11
[
58
]
Free,
GNU AGPLv3
,
Proprietary
Java
,
Python
,
JavaScript
TypeDB is a strongly-typed database software with an extensible type system.
Tarantool Graph DB
1.2.0
2024-01-01
[
59
]
Proprietary
Lua
,
C
Tarantool Graph DB is a graph-vector database. Analyze data connections in real time using a high-speed graph and vector storage
Graph query-programming languages
[
edit
]
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.
[
60
]
GQL
: proposed ISO standard graph query language
Gremlin
: a graph programming language that is a part of Apache TinkerPop open-source project
[
61
]
SPARQL
: a query language for RDF databases that can retrieve and manipulate data stored in RDF format
regular path queries
, a theoretical language for queries on graph databases
Graph transformation
– Creating a new graph from an existing graph
Hierarchical database model
– Tree-like structure for data
Datalog
– Declarative logic programming language
Vadalog
– Type of Knowledge Graph Management System
Object database
– Database presenting data as objects
RDF Database
– Database for storage and retrieval of triples
Structured storage
– Database class for storage and retrieval of modeled data
Text graph
– Text-structure representation using graph models
Vector database
– Type of database that uses vectors to represent other data
Wikidata
– Collaborative multilingual knowledge 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.
^
Bourbakis, Nikolaos G. (1998).
Artificial Intelligence and Automation
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ISBN
9789810226374
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2018-04-20
.
^
Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young (March 2017).
"Use of Graph Database for the Integration of Heterogeneous Biological Data"
.
Genomics & Informatics
.
15
(1):
19–
27.
doi
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10.5808/GI.2017.15.1.19
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ISSN
1598-866X
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PMC
5389944
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PMID
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40
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1–
39.
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207166126
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on 15 August 2017
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28 May
2016
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network models [...] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation
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b
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c
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^
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^
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. |
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- [1 History](https://en.wikipedia.org/wiki/Graph_database#History)
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- [3 Graph models](https://en.wikipedia.org/wiki/Graph_database#Graph_models)
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- [3\.1 Labeled-property graph](https://en.wikipedia.org/wiki/Graph_database#Labeled-property_graph)
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- [9 See also](https://en.wikipedia.org/wiki/Graph_database#See_also)
- [10 References](https://en.wikipedia.org/wiki/Graph_database#References)
- [11 Further reading](https://en.wikipedia.org/wiki/Graph_database#Further_reading)
- [12 External links](https://en.wikipedia.org/wiki/Graph_database#External_links)
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# Graph database
18 languages
- [Azərbaycanca](https://az.wikipedia.org/wiki/Qrafik_veril%C9%99nl%C9%99r_bazas%C4%B1 "Qrafik verilənlər bazası – Azerbaijani")
- [Català](https://ca.wikipedia.org/wiki/Base_de_dades_orientada_a_grafs "Base de dades orientada a grafs – Catalan")
- [کوردی](https://ckb.wikipedia.org/wiki/%D8%A8%D9%86%DA%A9%DB%95%D8%AF%D8%B1%D8%A7%D9%88%DB%95%DB%8C_%DA%AF%D8%B1%D8%A7%D9%81 "بنکەدراوەی گراف – Central Kurdish")
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From Wikipedia, the free encyclopedia
Database using graph structures for queries
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A **graph database** (**GDB**) is a [database](https://en.wikipedia.org/wiki/Database "Database") that uses [graph structures](https://en.wikipedia.org/wiki/Graph_\(data_structure\) "Graph (data structure)") for [semantic queries](https://en.wikipedia.org/wiki/Semantic_query "Semantic query") with [nodes](https://en.wikipedia.org/wiki/Node_\(graph_theory\) "Node (graph theory)"), [edges](https://en.wikipedia.org/wiki/Edge_\(graph_theory\) "Edge (graph theory)"), and properties to represent and store data.[\[1\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-1) A key concept of the system is the [graph](https://en.wikipedia.org/wiki/Graph_\(discrete_mathematics\) "Graph (discrete mathematics)") (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.[\[2\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:0-2)
Graph databases are commonly referred to as a [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") database. Graph databases are similar to 1970s [network model](https://en.wikipedia.org/wiki/Network_model "Network model") databases in that both represent general graphs, but network-model databases operate at a lower level of [abstraction](https://en.wikipedia.org/wiki/Abstraction_\(computer_science\) "Abstraction (computer science)")[\[3\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Gutierrez2-3) and lack easy [traversal](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal") over a chain of edges.[\[4\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-4)
The underlying storage mechanism of graph databases can vary. Relationships are first-class citizens in a graph database and can be labeled, directed, and given properties. Some depend on a relational engine and store the graph data in a [table](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)") (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 [key–value store](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair") or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") for storage, making them inherently NoSQL structures.
As of 2021[\[update\]](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit), no graph query language has been universally adopted in the same way as [SQL](https://en.wikipedia.org/wiki/SQL "SQL") was for [relational databases](https://en.wikipedia.org/wiki/Relational_database "Relational database"). There is a wide variety of systems used, many of which are tightly tied to one product. Early standardization efforts led to multi-vendor query languages like [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"), [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), and [Cypher](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language"). 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)[\[5\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-5). [GQL](https://en.wikipedia.org/wiki/Graph_Query_Language "Graph Query Language") 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](https://en.wikipedia.org/wiki/Application_programming_interface "Application programming interface") (APIs).
Graph databases differ from graph compute engines. Graph databases are technologies that are translations of the relational [online transaction processing](https://en.wikipedia.org/wiki/Online_transaction_processing "Online transaction processing") (OLTP) databases. On the other hand, graph compute engines are used in [online analytical processing](https://en.wikipedia.org/wiki/Online_analytical_processing "Online analytical processing") (OLAP) for bulk analysis.[\[6\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-6) Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations using proprietary graph databases,[\[7\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-7) along with the introduction of [open-source](https://en.wikipedia.org/wiki/Open-source_software "Open-source software") graph databases.
One study concluded that an RDBMS was "comparable" in performance to existing graph analysis engines at executing graph queries.[\[8\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-8)
## History
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=1 "Edit section: History")\]
In the mid-1960s, [navigational databases](https://en.wikipedia.org/wiki/Navigational_database "Navigational database") such as [IBM](https://en.wikipedia.org/wiki/IBM "IBM")'s [IMS](https://en.wikipedia.org/wiki/IBM_Information_Management_System "IBM Information Management System") supported [tree](https://en.wikipedia.org/wiki/Tree_\(data_structure\) "Tree (data structure)")\-like structures in its [hierarchical model](https://en.wikipedia.org/wiki/Hierarchical_database_model "Hierarchical database model"), but the strict [tree structure](https://en.wikipedia.org/wiki/Tree_structure "Tree structure") could be circumvented with virtual records.[\[9\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-9)[\[10\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-10)
Graph structures could be represented in network model databases from the late 1960s. [CODASYL](https://en.wikipedia.org/wiki/CODASYL "CODASYL"), which had defined [COBOL](https://en.wikipedia.org/wiki/COBOL "COBOL") in 1959, defined the Network Database Language in 1969.
[Labeled graphs](https://en.wikipedia.org/wiki/Graph_labeling "Graph labeling") could be represented in graph databases from the mid-1980s, such as the Logical Data Model.[\[11\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Gutierrez-11)[\[12\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-12)
Commercial [object databases](https://en.wikipedia.org/wiki/Object_database "Object database") (ODBMSs) emerged in the early 1990s. In 2000, the [Object Data Management Group](https://en.wikipedia.org/wiki/Object_Data_Management_Group "Object Data Management Group") published a standard language for defining object and relationship (graph) structures in their ODMG'93 publication.\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\]
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](https://en.wikipedia.org/wiki/ACID "ACID") guarantees such as [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") and [Oracle Spatial and Graph](https://en.wikipedia.org/wiki/Oracle_Spatial_and_Graph "Oracle Spatial and Graph") became available.
In the 2010s, commercial ACID graph databases that could be [scaled horizontally](https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling "Scalability") became available. Further, [SAP HANA](https://en.wikipedia.org/wiki/SAP_HANA "SAP HANA") brought [in-memory](https://en.wikipedia.org/wiki/In-memory_database "In-memory database") and [columnar](https://en.wikipedia.org/wiki/Column-oriented_DBMS "Column-oriented DBMS") technologies to graph databases.[\[13\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-13) Also in the 2010s, [multi-model databases](https://en.wikipedia.org/wiki/Multi-model_database "Multi-model database") that supported graph models (and other models such as relational database or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database")) became available, such as [OrientDB](https://en.wikipedia.org/wiki/OrientDB "OrientDB"), [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB"), and [MarkLogic](https://en.wikipedia.org/wiki/MarkLogic "MarkLogic") (starting with its 7.0 version). During this time, graph databases of various types have become especially popular with [social network analysis](https://en.wikipedia.org/wiki/Social_network_analysis "Social network analysis") with the advent of social media companies. Also during the decade, [cloud](https://en.wikipedia.org/wiki/Cloud_computing "Cloud computing")\-based graph databases such as [Amazon Neptune](https://en.wikipedia.org/wiki/Amazon_Neptune "Amazon Neptune") and [Neo4j AuraDB](https://en.wikipedia.org/wiki/Neo4j#Licensing_and_editions "Neo4j") became available.
## Background
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=2 "Edit section: Background")\]
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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](https://en.wikipedia.org/wiki/Graph_theory "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](https://en.wikipedia.org/wiki/Row_\(database\) "Row (database)") 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](https://en.wikipedia.org/wiki/Relational_model "Relational model") or a [document-store model](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database")**.**
- **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
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=3 "Edit section: Graph models")\]
### Labeled-property graph
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=4 "Edit section: Labeled-property graph")\]
[](https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.svg)
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 [key–value pairs](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair"). 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;[\[14\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-14) making the graph a [directed graph](https://en.wikipedia.org/wiki/Directed_graph "Directed graph"). Relationships can also have properties. This is useful in providing additional metadata and semantics to relationships of the nodes.[\[15\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-15) Direct storage of relationships allows a [constant-time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time "Time complexity") [traversal](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal").[\[16\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:32-16)
### Resource Description Framework (RDF)
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=5 "Edit section: Resource Description Framework (RDF)")\]
Main article: [RDF (computer science)](https://en.wikipedia.org/wiki/RDF_\(computer_science\) "RDF (computer science)")
[](https://en.wikipedia.org/wiki/File:Rdf-graph3.png)
An example RDF graph
In an [RDF](https://en.wikipedia.org/wiki/RDF_\(computer_science\) "RDF (computer science)") 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 `hasName` 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](https://en.wikipedia.org/wiki/Literal_\(computer_programming\) "Literal (computer programming)") and/or be identified by a [URI](https://en.wikipedia.org/wiki/Uniform_Resource_Identifier "Uniform Resource Identifier"). 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 [URI](https://en.wikipedia.org/wiki/Uniform_Resource_Identifier "Uniform Resource Identifier").[\[17\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-17)
## Properties
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=6 "Edit section: Properties")\]
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.\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\]
### Storage
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=7 "Edit section: Storage")\]
The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and "store" the graph data in a [table](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)") (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](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair") or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") for storage, making them inherently [NoSQL](https://en.wikipedia.org/wiki/NoSQL "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
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=8 "Edit section: Index-free adjacency")\]
Data lookup performance is dependent on the access speed from one particular node to another. Because [index](https://en.wikipedia.org/wiki/Database_index "Database index")\-free adjacency enforces the nodes to have direct physical [RAM](https://en.wikipedia.org/wiki/Random-access_memory "Random-access memory") 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](https://en.wikipedia.org/wiki/Cache_\(computing\) "Cache (computing)") 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 traversals](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal"). Native graph databases use index-free adjacency to process [CRUD](https://en.wikipedia.org/wiki/CRUD "CRUD") operations on the stored data.
## Applications
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=9 "Edit section: Applications")\]
Multiple categories of graphs by kind of data have been recognised. Gartner suggests the five broad categories of graphs:[\[18\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-18)
- [Social graph](https://en.wikipedia.org/wiki/Social_graph "Social graph"): this is about the connections between people; examples include [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook"), [Twitter](https://en.wikipedia.org/wiki/Twitter "Twitter"), and the idea of [six degrees of separation](https://en.wikipedia.org/wiki/Six_degrees_of_separation "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](https://en.wikipedia.org/wiki/Interest_graph "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](https://en.wikipedia.org/wiki/Internet_of_things "Internet of things") (IoT) devices.
## Comparison with relational databases
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=10 "Edit section: Comparison with relational databases")\]
Since [Edgar F. Codd](https://en.wikipedia.org/wiki/Edgar_F._Codd "Edgar F. Codd")'s 1970 paper on the [relational model](https://en.wikipedia.org/wiki/Relational_model "Relational model"),[\[19\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:2-19) [relational databases](https://en.wikipedia.org/wiki/Relational_databases "Relational databases") have been the de facto industry standard for large-scale data storage systems. Relational models require a strict schema and [data normalization](https://en.wikipedia.org/wiki/Data_normalization "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](https://en.wikipedia.org/wiki/Data_consistency "Data consistency") and support [ACID transactions](https://en.wikipedia.org/wiki/ACID_\(computer_science\) "ACID (computer science)"). 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.[\[19\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:2-19) 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](https://en.wikipedia.org/wiki/Foreign_key "Foreign key") constraints should also be considered when retrieving relationships, causing additional overhead.
Compared with [relational databases](https://en.wikipedia.org/wiki/Relational_database "Relational database"), graph databases are often faster for associative data sets[\[20\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-20) and map more directly to the structure of [object-oriented](https://en.wikipedia.org/wiki/Object-oriented_programming "Object-oriented programming") applications. They can scale more naturally[\[21\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-21) to large datasets as they do not typically need [join](https://en.wikipedia.org/wiki/Join_\(SQL\) "Join (SQL)") 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 [\[22\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-22) 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.[\[23\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:12-23)
### Examples
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=11 "Edit section: Examples")\]
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 [tables](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)"), 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 [indexes](https://en.wikipedia.org/wiki/Database_index "Database index"), which allow data to be stored in a smaller sub-table, containing only the selected data and a [unique key](https://en.wikipedia.org/wiki/Unique_key "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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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](https://en.wikipedia.org/wiki/Primary_key "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](https://en.wikipedia.org/wiki/Join_\(SQL\) "Join (SQL)"), 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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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 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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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 [backlinks](https://en.wikipedia.org/wiki/Backlink "Backlink") 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](https://en.wikipedia.org/wiki/Big_O_notation "Big O notation"), this query would be O ( log n ) \+ O ( 1 ) {\\displaystyle O(\\log n)+O(1)}  time – i.e., proportional to the logarithm of the size of the data. In contrast, the relational version would be multiple O ( log n ) {\\displaystyle O(\\log n)}  lookups, plus the O ( n ) {\\displaystyle O(n)}  time needed to join all of the data records.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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](https://en.wikipedia.org/wiki/Gone_with_the_Wind_\(film\) "Gone with the Wind (film)")*". 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](https://en.wikipedia.org/wiki/Clark_Gable "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.[\[25\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-examples-25)
*Properties* add another layer of [abstraction](https://en.wikipedia.org/wiki/Abstraction_\(computer_science\) "Abstraction (computer science)") 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](https://en.wikipedia.org/wiki/Vivien_Leigh "Vivien Leigh"), [Olivia de Havilland](https://en.wikipedia.org/wiki/Olivia_de_Havilland "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.[\[25\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-examples-25)
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](https://en.wikipedia.org/wiki/Social_networking "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](https://en.wikipedia.org/wiki/Big_data "Big data") environments. For this reason, graph databases are becoming very popular for large online systems like [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook"), [Google](https://en.wikipedia.org/wiki/Google "Google"), [Twitter](https://en.wikipedia.org/wiki/Twitter "Twitter"), and similar systems with deep links between records.
To further illustrate, imagine a relational model with two tables: a `people` table (which has a `person_id` and `person_name` column) and a `friend` table (with `friend_id` and `person_id`, which is a [foreign key](https://en.wikipedia.org/wiki/Foreign_key "Foreign key") from the `people` table). In this case, searching for all of Jack's friends would result in the following SQL query.
```
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';
```
The same query may be translated into --
- [Cypher](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language"), a graph database [query language](https://en.wikipedia.org/wiki/Query_language "Query language")
```
MATCH (p1:person {name: 'Jack'})-[:FRIEND_WITH]-(p2:person)
RETURN p2.name
```
- [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(query_language\) "Gremlin (query language)"), an imperative style graph [query language](https://en.wikipedia.org/wiki/Query_language "Query language") maintained by Apache TinkerPop and used by many graph databases[\[26\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-26)
```
g.V().hasLabel("person").has("name", "Jack").
out("friendsWith").
hasLabel("person").
values("name")
```
- [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), an RDF graph database [query language](https://en.wikipedia.org/wiki/Query_language "Query language") standardized by [W3C](https://en.wikipedia.org/wiki/W3C "W3C") and used in multiple RDF [Triple](https://en.wikipedia.org/wiki/Triplestore "Triplestore") and [Quad](https://en.wikipedia.org/wiki/Named_graph "Named graph") stores
- Long form
```
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 .
}
```
- Short form
```
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE { ?s foaf:name "Jack" ;
foaf:knows ?o .
?o foaf:name ?name .
}
```
- SPASQL, a hybrid database query language, that extends [SQL](https://en.wikipedia.org/wiki/SQL "SQL") with [SPARQL](https://en.wikipedia.org/wiki/SPARQL "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 ;
```
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.[\[16\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:32-16)
## List of graph databases
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=12 "Edit section: List of graph databases")\]
The following is a list of [notable](https://en.wikipedia.org/wiki/Wikipedia:GNG "Wikipedia:GNG") graph databases:
| Name | Current version | Latest release date (YYYY-MM-DD) | [Software license](https://en.wikipedia.org/wiki/Software_license "Software license") | [Programming language](https://en.wikipedia.org/wiki/Programming_language "Programming language") | Description |
|---|---|---|---|---|---|
| [Aerospike](https://en.wikipedia.org/wiki/Aerospike_\(database\) "Aerospike (database)") | 7\.0 | 2024-05-15 | 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](https://en.wikipedia.org/w/index.php?title=AgensGraph&action=edit&redlink=1 "AgensGraph (page does not exist)")[\[27\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-27) | 2\.16.0 | 2025-09-12[\[28\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-28) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") Community version, [proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") 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](https://en.wikipedia.org/wiki/PostgreSQL "PostgreSQL") RDBMS, providing a highly reliable, fully-featured platform ready for enterprise use. |
| [AllegroGraph](https://en.wikipedia.org/wiki/AllegroGraph "AllegroGraph") | 7\.0.0 | 2022-12-20 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), clients: [Eclipse Public License](https://en.wikipedia.org/wiki/Eclipse_Public_License "Eclipse Public License") v1 | [C\#](https://en.wikipedia.org/wiki/C_Sharp_\(programming_language\) "C Sharp (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [Common Lisp](https://en.wikipedia.org/wiki/Common_Lisp "Common Lisp"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | [Resource Description Framework](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework") (RDF) and graph database. |
| [Amazon Neptune](https://en.wikipedia.org/wiki/Amazon_Neptune "Amazon Neptune") | 1\.4.7.0 | 2026-03-03[\[29\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-29) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | Not disclosed | Amazon Neptune is a fully managed graph database by [Amazon.com](https://en.wikipedia.org/wiki/Amazon.com "Amazon.com"). It is used as a [web service](https://en.wikipedia.org/wiki/Web_service "Web service"), and is part of [Amazon Web Services](https://en.wikipedia.org/wiki/Amazon_Web_Services "Amazon Web Services"). Supports popular graph models property graph and [W3C](https://en.wikipedia.org/wiki/W3C "W3C")'s [RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework"), and their respective [query languages](https://en.wikipedia.org/wiki/Query_language "Query language") Apache TinkerPop, [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"), [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), and [openCypher](https://en.wikipedia.org/wiki/Cypher_\(query_language\) "Cypher (query language)"). |
| [Altair Graph Studio](https://en.wikipedia.org/wiki/Altair_Engineering "Altair Engineering") | 6\.3 | 2025-12 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | AnzoGraph DB is a [massively parallel](https://en.wikipedia.org/wiki/Massively_parallel "Massively parallel") native Graph Online Analytics Processing ([GOLAP](https://en.wikipedia.org/w/index.php?title=GOLAP&action=edit&redlink=1 "GOLAP (page does not exist)")) style database built to support [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL") and [Cypher Query Language](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language") to analyze trillions of relationships. AnzoGraph DB is designed for interactive analysis of large sets of [semantic triple](https://en.wikipedia.org/wiki/Semantic_triple "Semantic triple") data, but also supports labeled properties under proposed [W3C](https://en.wikipedia.org/wiki/W3C "W3C") standards.[\[30\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-30)[\[31\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-31)[\[32\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-32)[\[33\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-33) |
| [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB") | 3\.12.4.2 | 2025-04-09 | [Free](https://en.wikipedia.org/wiki/Free_software "Free software") [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License"), [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript"), [.NET](https://en.wikipedia.org/wiki/.NET ".NET"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [Node.js](https://en.wikipedia.org/wiki/Node.js "Node.js"), [PHP](https://en.wikipedia.org/wiki/PHP "PHP"), [Scala](https://en.wikipedia.org/wiki/Scala_\(programming_language\) "Scala (programming language)"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Ruby](https://en.wikipedia.org/wiki/Ruby_\(programming_language\) "Ruby (programming language)"), [Elixir](https://en.wikipedia.org/wiki/Elixir_\(programming_language\) "Elixir (programming language)") | [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") native graph database system developed by ArangoDB Inc, supporting three data models (key/value, documents, graphs, vector), 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](https://en.wikipedia.org/wiki/Cosmos_DB "Cosmos DB") | | 2017 | Proprietary | Not disclosed | Multi-modal database which supports graph concepts using the [Apache Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(query_language\) "Gremlin (query language)") query language |
| [DataStax](https://en.wikipedia.org/wiki/DataStax "DataStax") Enterprise Graph | v6.0.1 | 2018-06 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Distributed, real-time, scalable database; supports Tinkerpop, and integrates with [Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra "Apache Cassandra")[\[34\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-34) |
| [FalkorDB](https://github.com/FalkorDB/FalkorDB) | 4\.16 | 2026-01 | [SSPLv1](https://en.wikipedia.org/wiki/Server_Side_Public_License "Server Side Public License") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [Rust](https://en.wikipedia.org/wiki/Rust_\(programming_language\) "Rust (programming language)") | High-performance, in-memory graph database that uses sparse matrix multiplication ([GraphBLAS](https://en.wikipedia.org/wiki/GraphBLAS "GraphBLAS")) for query execution; it is the community-led successor to [RedisGraph](https://en.wikipedia.org/wiki/Redis "Redis") following its end-of-life; optimized for low-latency [GraphRAG](https://en.wikipedia.org/wiki/Retrieval-augmented_generation "Retrieval-augmented generation") and AI applications. |
| [Graph in Microsoft Fabric](https://learn.microsoft.com/fabric/graph/overview) | | 2025-10 | Proprietary | Not disclosed | Microsoft's first native, horizontally scalable graph data platform that integrates data management, analytics, and visualization. It uses [GQL](https://en.wikipedia.org/wiki/Graph_Query_Language "Graph Query Language") as the query language. |
| [Google Spanner Graph](https://en.wikipedia.org/wiki/Google_Spanner "Google Spanner") | N/A | 2025-01 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | A horizontally scalable, multi-model database that provides a native graph experience on top of [Spanner](https://en.wikipedia.org/wiki/Spanner_\(database\) "Spanner (database)"); supports the [ISO](https://en.wikipedia.org/wiki/ISO "ISO") [GQL](https://en.wikipedia.org/w/index.php?title=GQL_\(database_query_language\)&action=edit&redlink=1 "GQL (database query language) (page does not exist)") standard and [openCypher](https://en.wikipedia.org/wiki/Cypher_\(query_language\) "Cypher (query language)") for matching patterns and traversing relationships; designed for global consistency and trillions of edges. |
| [GUN (Graph Universe Node)](https://en.wikipedia.org/wiki/GUN_\(graph_database\) "GUN (graph database)") | 0\.2020.1240 | 2024 | Open source, [MIT License](https://en.wikipedia.org/wiki/MIT_License "MIT License"), [Apache 2.0](https://en.wikipedia.org/wiki/Apache_License "Apache License"), [zlib License](https://en.wikipedia.org/wiki/Zlib_License "Zlib License") | [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") | An [open source](https://en.wikipedia.org/wiki/Open_source "Open source"), [offline-first](https://en.wikipedia.org/wiki/Online_and_offline "Online and offline"), [real-time](https://en.wikipedia.org/wiki/Real-time_communication "Real-time communication"), [decentralized](https://en.wikipedia.org/wiki/Decentralized_web "Decentralized web"), graph database written in [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") for the [web browser](https://en.wikipedia.org/wiki/Web_browser "Web browser").[\[35\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-35)[\[36\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-36) It is implemented as a [peer-to-peer](https://en.wikipedia.org/wiki/Peer-to-peer "Peer-to-peer") network featuring [multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication "Multi-master replication") with a custom [commutative replicated data type (CRDT)](https://en.wikipedia.org/wiki/Conflict-free_replicated_data_type "Conflict-free replicated data type").\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\] |
| [InfiniteGraph](https://en.wikipedia.org/wiki/InfiniteGraph "InfiniteGraph") | 2021\.2 | 2021-05 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [commercial, free 50GB version](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/JanusGraph "JanusGraph") | 1\.1.0 | 2024-11-07[\[37\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-37) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Open source, scalable, distributed across a multi-machine cluster graph database under The [Linux Foundation](https://en.wikipedia.org/wiki/Linux_Foundation "Linux Foundation"); supports various storage backends ([Apache Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra "Apache Cassandra"), [Apache HBase](https://en.wikipedia.org/wiki/Apache_HBase "Apache HBase"), Google Cloud [Bigtable](https://en.wikipedia.org/wiki/Bigtable "Bigtable"), Oracle [Berkeley DB](https://en.wikipedia.org/wiki/Berkeley_DB "Berkeley DB"));[\[38\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-38) supports global graph data analytics, reporting, and [extract, transform, load](https://en.wikipedia.org/wiki/Extract,_transform,_load "Extract, transform, load") (ETL) through integration with big data platforms ([Apache Spark](https://en.wikipedia.org/wiki/Apache_Spark "Apache Spark"), [Apache Giraph](https://en.wikipedia.org/wiki/Apache_Giraph "Apache Giraph"), [Apache Hadoop](https://en.wikipedia.org/wiki/Apache_Hadoop "Apache Hadoop")); supports geo, numeric range, and full-text search via external index storages ([Elasticsearch](https://en.wikipedia.org/wiki/Elasticsearch "Elasticsearch"), [Apache Solr](https://en.wikipedia.org/wiki/Apache_Solr "Apache Solr"), [Apache Lucene](https://en.wikipedia.org/wiki/Apache_Lucene "Apache Lucene")).[\[39\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-39) |
| [Kùzu](https://github.com/kuzudb/kuzu) | 0\.11.3 | 2025-10 | [MIT](https://en.wikipedia.org/wiki/MIT_License "MIT License") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | Embeddable, open-source graph database management system (GDBMS) featuring a vectorized query engine; the project was abandoned by its creator and sponsor Kùzu Inc. in October 2025, with the repository archived and documentation moved to GitHub.[\[40\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-40) |
| [Ladybug](https://github.com/LadybugDB/ladybug) | 0\.15.1 | 2026-03 | [MIT](https://en.wikipedia.org/wiki/MIT_License "MIT License") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | Embedded columnar graph database, based on a fork of Kùzu. Supports querying Apache Parquet and Apache Arrow with zero copy.[\[41\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-41) |
| [MarkLogic](https://en.wikipedia.org/wiki/MarkLogic "MarkLogic") | 8\.0.4 | 2015 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") developer version | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Multi-model [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") database that stores [documents](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") (JSON and XML) and semantic graph data ([RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework") triples); also has a built-in search engine. |
| [Microsoft SQL Server](https://en.wikipedia.org/wiki/Microsoft_SQL_Server "Microsoft SQL Server") 2017 | RC1 | | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [SQL](https://en.wikipedia.org/wiki/SQL "SQL")/T-SQL, [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | 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.[\[42\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-42) |
| [NebulaGraph](https://en.wikipedia.org/wiki/NebulaGraph "NebulaGraph") | 3\.8.0 | 2024-05 | Open Source Edition is under Apache 2.0, Common Clause 1.0 | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | 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.[\[43\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-43) |
| [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") | 2026\.03.1 | 2026-04-02[\[44\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-44) | [GPLv3](https://en.wikipedia.org/wiki/GNU_General_Public_License "GNU General Public License") Community Edition, [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") and [AGPLv3](https://en.wikipedia.org/wiki/Affero_General_Public_License "Affero General Public License") options for enterprise and advanced editions | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [.NET](https://en.wikipedia.org/wiki/.NET ".NET"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Ruby](https://en.wikipedia.org/wiki/Ruby_\(programming_language\) "Ruby (programming language)"), [PHP](https://en.wikipedia.org/wiki/PHP "PHP"), [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)"), [Erlang](https://en.wikipedia.org/wiki/Erlang_\(programming_language\) "Erlang (programming language)")/[Elixir](https://en.wikipedia.org/wiki/Elixir_\(programming_language\) "Elixir (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)")/[C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Clojure](https://en.wikipedia.org/wiki/Clojure "Clojure"), [Perl](https://en.wikipedia.org/wiki/Perl "Perl"), [Haskell](https://en.wikipedia.org/wiki/Haskell "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 [REST](https://en.wikipedia.org/wiki/Representational_state_transfer "Representational state transfer") [web API](https://en.wikipedia.org/wiki/Web_API "Web API") interface, and a proprietary Bolt protocol with official drivers. |
| [Ontotext GraphDB](https://en.wikipedia.org/wiki/Ontotext_GraphDB "Ontotext GraphDB") | 11\.3.1 | 2026-02-19[\[45\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-45) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), Standard and Enterprise Editions are [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software"), Free Edition is [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Highly efficient and robust semantic graph database with RDF and SPARQL support, also available as a high-availability cluster. Integrates [OpenRefine](https://en.wikipedia.org/wiki/OpenRefine "OpenRefine") for ingestion and reconciliation of tabular data and [ontop](https://en.wikipedia.org/wiki/Ontop "Ontop") for [Ontology-Based Data Access](https://en.wikipedia.org/w/index.php?title=Ontology-Based_Data_Access&action=edit&redlink=1 "Ontology-Based Data Access (page does not exist)"). Connects to [Lucene](https://en.wikipedia.org/wiki/Apache_Lucene "Apache Lucene"), [SOLR](https://en.wikipedia.org/wiki/Apache_Solr "Apache Solr") and [Elasticsearch](https://en.wikipedia.org/wiki/Elasticsearch "Elasticsearch") for [Full text](https://en.wikipedia.org/wiki/Full_text_search "Full text search") and [Faceted search](https://en.wikipedia.org/wiki/Faceted_search "Faceted search"), and [Kafka](https://en.wikipedia.org/wiki/Apache_Kafka "Apache Kafka") for event and stream processing. Supports [OGC](https://en.wikipedia.org/wiki/Open_Geospatial_Consortium "Open Geospatial Consortium") [GeoSPARQL](https://en.wikipedia.org/wiki/GeoSPARQL "GeoSPARQL"). Provides [JDBC](https://en.wikipedia.org/wiki/Java_Database_Connectivity "Java Database Connectivity") access to [Knowledge Graphs](https://en.wikipedia.org/wiki/Knowledge_graph "Knowledge graph").[\[46\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-46) |
| OpenLink [Virtuoso](https://en.wikipedia.org/wiki/Virtuoso_Universal_Server "Virtuoso Universal Server") | 8\.2 | 2018-10 | Open Source Edition is [GPLv2](https://en.wikipedia.org/wiki/GNU_General_Public_License "GNU General Public License"), Enterprise Edition is [proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/NEPOMUK_\(software\) "NEPOMUK (software)") Semantic Desktop), a one-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server[\[47\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Virtuoso_Clustering_Diagrams-47) |
| Oracle RDF Graph; part of [Oracle Database](https://en.wikipedia.org/wiki/Oracle_Database "Oracle Database") | 21c | 2020 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), [SQL](https://en.wikipedia.org/wiki/SQL "SQL") | RDF Graph capabilities as features in multi-model Oracle Database: RDF Graph: comprehensive [W3C](https://en.wikipedia.org/wiki/W3C "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 | [PGQL](https://en.wikipedia.org/wiki/Graph_Query_Language#PGQL "Graph Query Language"), 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](https://en.wikipedia.org/wiki/SQL "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](https://en.wikipedia.org/wiki/OrientDB "OrientDB") | 3\.2.28 | 2024-02 | Community Edition is [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License"), Enterprise Edition is [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Second-generation[\[48\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-48) 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](https://en.wikipedia.org/wiki/ACID "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](https://en.wikipedia.org/wiki/SQL "SQL"). It has HTTP [REST](https://en.wikipedia.org/wiki/Representational_state_transfer "Representational state transfer") and [JSON](https://en.wikipedia.org/wiki/JSON "JSON") [API](https://en.wikipedia.org/wiki/API "API"). |
| [RedisGraph](https://en.wikipedia.org/wiki/Redis_Labs "Redis Labs") | 2\.0.20 | 2020-09 | Redis Source Available License,[AGPLv3](https://en.wikipedia.org/wiki/Affero_General_Public_License "Affero General Public License"),[SSPL](https://en.wikipedia.org/wiki/Server_Side_Public_License "Server Side Public License") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)") | In-memory, queryable Property Graph database which uses [sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix "Sparse matrix") to represent the [adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix "Adjacency matrix") in graphs and [linear algebra](https://en.wikipedia.org/wiki/Linear_algebra "Linear algebra") to query the graph.[\[49\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-49) |
| [SAP HANA](https://en.wikipedia.org/wiki/SAP_HANA "SAP HANA") | 2\.0 SPS 05 | 2020-06[\[50\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-50) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") and [SQL](https://en.wikipedia.org/wiki/SQL "SQL")\-like language | In-memory [ACID](https://en.wikipedia.org/wiki/ACID "ACID") transaction supported property graph[\[51\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-SapHana-51) |
| [Sparksee](https://en.wikipedia.org/wiki/Sparksee_\(graph_database\) "Sparksee (graph database)") | 5\.2.0 | 2015 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software"), [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") for evaluation, research, development | [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), C++, [C\#](https://en.wikipedia.org/wiki/C_Sharp_\(programming_language\) "C Sharp (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), and [Objective-C](https://en.wikipedia.org/wiki/Objective-C "Objective-C"); version 5 is the first graph [mobile database](https://en.wikipedia.org/wiki/Mobile_database "Mobile database"). |
| [Teradata Aster](https://en.wikipedia.org/wiki/Teradata#Aster_Platform "Teradata") | 7 | 2016 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [SQL](https://en.wikipedia.org/wiki/SQL "SQL"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)") | [Massive parallel processing](https://en.wikipedia.org/wiki/Massive_parallel_processing "Massive parallel processing") (MPP) database incorporating patented engines supporting native SQL, [MapReduce](https://en.wikipedia.org/wiki/MapReduce "MapReduce"), and graph data storage and manipulation; provides a set of analytic function libraries and data visualization[\[52\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-52) |
| [TerminusDB](https://en.wikipedia.org/wiki/TerminusDB "TerminusDB") | 11\.0.6 | 2023-05-03[\[53\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-53) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Prolog](https://en.wikipedia.org/wiki/Prolog "Prolog"), [Rust](https://en.wikipedia.org/wiki/Rust_\(programming_language\) "Rust (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [JSON-LD](https://en.wikipedia.org/wiki/JSON-LD "JSON-LD") | Document-oriented knowledge graph; the power of an enterprise knowledge graph with the simplicity of documents. |
| [TigerGraph](https://en.wikipedia.org/wiki/TigerGraph "TigerGraph") | 4\.1.2 | 2024-12-20[\[54\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-54) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | [Massive parallel processing](https://en.wikipedia.org/wiki/Massive_parallel_processing "Massive parallel processing") (MPP) native graph database management system[\[55\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Forrester-55) |
| TinkerGraph | 3\.8.0 | 2025-11-12[\[56\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-56) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | TinkerGraph is a single machine, in-memory (with optional persistence), graph engine that provides both OLTP and OLAP functionality. TinkerGraph is deployed with Apache TinkerPop and serves as the reference implementation for other providers to study in order to understand the semantics of the various methods of the TinkerPop API.[\[57\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-57) |
| [TypeDB](https://en.wikipedia.org/wiki/GRAKN.AI "GRAKN.AI") | 2\.14.0 | 2022-11[\[58\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-58) | Free, [GNU AGPLv3](https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License "GNU Affero General Public License"), [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") | TypeDB is a strongly-typed database software with an extensible type system. |
| Tarantool Graph DB | 1\.2.0 | 2024-01-01[\[59\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-59) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Lua](https://en.wikipedia.org/wiki/Lua_\(programming_language\) "Lua (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)") | Tarantool Graph DB is a graph-vector database. Analyze data connections in real time using a high-speed graph and vector storage |
## Graph query-programming languages
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=13 "Edit section: Graph query-programming languages")\]
- [AQL (ArangoDB Query Language)](https://en.wikipedia.org/wiki/AQL_\(ArangoDB_Query_Language\) "AQL (ArangoDB Query Language)"): a SQL-like query language used in [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB") for both documents and graphs
- [Cypher Query Language](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language") (Cypher): a graph query [declarative language](https://en.wikipedia.org/wiki/Declarative_language "Declarative language") for [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") that enables ad hoc and programmatic (SQL-like) access to the graph.[\[60\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-60)
- [GQL](https://en.wikipedia.org/wiki/GQL_Graph_Query_Language "GQL Graph Query Language"): proposed ISO standard graph query language
- [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"): a graph programming language that is a part of Apache TinkerPop open-source project[\[61\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-61)
- [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"): a query language for RDF databases that can retrieve and manipulate data stored in RDF format
- [regular path queries](https://en.wikipedia.org/wiki/Regular_path_query "Regular path query"), a theoretical language for queries on graph databases
## See also
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=14 "Edit section: See also")\]
- [Graph transformation](https://en.wikipedia.org/wiki/Graph_transformation "Graph transformation") – Creating a new graph from an existing graphPages displaying short descriptions of redirect targets
- [Hierarchical database model](https://en.wikipedia.org/wiki/Hierarchical_database_model "Hierarchical database model") – Tree-like structure for data
- [Datalog](https://en.wikipedia.org/wiki/Datalog "Datalog") – Declarative logic programming language
- [Vadalog](https://en.wikipedia.org/wiki/Vadalog "Vadalog") – Type of Knowledge Graph Management System
- [Object database](https://en.wikipedia.org/wiki/Object_database "Object database") – Database presenting data as objects
- [RDF Database](https://en.wikipedia.org/wiki/RDF_Database "RDF Database") – Database for storage and retrieval of triplesPages displaying short descriptions of redirect targets
- [Structured storage](https://en.wikipedia.org/wiki/Structured_storage "Structured storage") – Database class for storage and retrieval of modeled dataPages displaying short descriptions of redirect targets
- [Text graph](https://en.wikipedia.org/wiki/Text_graph "Text graph") – Text-structure representation using graph models
- [Vector database](https://en.wikipedia.org/wiki/Vector_database "Vector database") – Type of database that uses vectors to represent other data
- [Wikidata](https://en.wikipedia.org/wiki/Wikidata "Wikidata") – Collaborative multilingual knowledge 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](https://en.wikipedia.org/wiki/SPARQL "SPARQL") queries.
## References
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=15 "Edit section: References")\]
1. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-1)**
Bourbakis, Nikolaos G. (1998). [*Artificial Intelligence and Automation*](https://books.google.com/books?id=mV3wxKLHlnwC&q=%22gdb%22+%22graph+database%22&pg=PA381). World Scientific. p. 381. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9789810226374](https://en.wikipedia.org/wiki/Special:BookSources/9789810226374 "Special:BookSources/9789810226374")
. Retrieved 2018-04-20.
2. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-:0_2-0)**
Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young (March 2017). ["Use of Graph Database for the Integration of Heterogeneous Biological Data"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389944). *Genomics & Informatics*. **15** (1): 19–27\. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10\.5808/GI.2017.15.1.19](https://doi.org/10.5808%2FGI.2017.15.1.19). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1598-866X](https://search.worldcat.org/issn/1598-866X). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [5389944](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389944). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [28416946](https://pubmed.ncbi.nlm.nih.gov/28416946).
3. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-Gutierrez2_3-0)**
Angles, Renzo; Gutierrez, Claudio (1 Feb 2008). ["Survey of graph database models"](https://web.archive.org/web/20170815064527/https://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf) (PDF). *ACM Computing Surveys*. **40** (1): 1–39\. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10\.1.1.110.1072](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.1072). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10\.1145/1322432.1322433](https://doi.org/10.1145%2F1322432.1322433). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [207166126](https://api.semanticscholar.org/CorpusID:207166126). Archived from [the original](http://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf) (PDF) on 15 August 2017. Retrieved 28 May 2016. "network models \[...\] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation"
4. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-4)**
Silberschatz, Avi (28 January 2010). [*Database System Concepts, Sixth Edition*](http://codex.cs.yale.edu/avi/db-book/db6/appendices-dir/d.pdf) (PDF). McGraw-Hill. p. D-29. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
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## Further reading
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=16 "Edit section: Further reading")\]
- Gosnell, Denise; Broecheler, Matthias (2020). *The Practitioner's Guide to Graph Data* (1st ed.). Sebastopol, CA: O’Reilly Media, Inc. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9781492044048](https://en.wikipedia.org/wiki/Special:BookSources/9781492044048 "Special:BookSources/9781492044048")
.
- Sun, Ricky (2024). *Essential Criteria of Graph Databases*. Elsevier. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9780443141621](https://en.wikipedia.org/wiki/Special:BookSources/9780443141621 "Special:BookSources/9780443141621")
.
- Bechberger, Dave; Perryman, Josh (2021). *Exploring Graph Databases*. Shelter Island, NY: Manning Publications. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9781617296376](https://en.wikipedia.org/wiki/Special:BookSources/9781617296376 "Special:BookSources/9781617296376")
.
- Singh, Ajit (2023). *Graph Database Modeling with Neo4j: Graph Schema Design Using ER-Model, Normalizing Graph Schema, Closeness Centrality, Pagerank, Metarule, Cypher Query Language, Neo4j SQL* (2nd ed.). United States: Independently Published. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9798351798783](https://en.wikipedia.org/wiki/Special:BookSources/9798351798783 "Special:BookSources/9798351798783")
.
## External links
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=17 "Edit section: External links")\]
- ["Graph Data Modeling: All You Need To Know"](https://www.puppygraph.com/blog/graph-data-modeling). *PuppyGraph*. Retrieved 2025-08-21.
| [v](https://en.wikipedia.org/wiki/Template:Database_models "Template:Database models") [t](https://en.wikipedia.org/wiki/Template_talk:Database_models "Template talk:Database models") [e](https://en.wikipedia.org/wiki/Special:EditPage/Template:Database_models "Special:EditPage/Template:Database models")[Database models](https://en.wikipedia.org/wiki/Database_model "Database model") | |
|---|---|
| Common models | [Flat](https://en.wikipedia.org/wiki/Flat-file_database "Flat-file database") [Hierarchical](https://en.wikipedia.org/wiki/Hierarchical_database_model "Hierarchical database model") [Dimensional](https://en.wikipedia.org/wiki/Dimensional_modeling "Dimensional modeling") [Network](https://en.wikipedia.org/wiki/Network_model "Network model") [Relational](https://en.wikipedia.org/wiki/Relational_model "Relational model") [Entity–relationship](https://en.wikipedia.org/wiki/Entity%E2%80%93relationship_model "Entity–relationship model") [Enhanced](https://en.wikipedia.org/wiki/Enhanced_entity%E2%80%93relationship_model "Enhanced entity–relationship model") [Graph]() [Object-oriented](https://en.wikipedia.org/wiki/Object_database "Object database") [Entity–attribute–value](https://en.wikipedia.org/wiki/Entity%E2%80%93attribute%E2%80%93value_model "Entity–attribute–value model") |
| Other models | [Multi-dimensional](https://en.wikipedia.org/wiki/Online_analytical_processing#Multidimensional_databases "Online analytical processing") [Array](https://en.wikipedia.org/wiki/Array_DBMS "Array DBMS") [Semantic](https://en.wikipedia.org/wiki/Semantic_data_model "Semantic data model") [Star schema](https://en.wikipedia.org/wiki/Star_schema "Star schema") [XML database](https://en.wikipedia.org/wiki/XML_database "XML database") |
| Implementations | [Flat file](https://en.wikipedia.org/wiki/Flat-file_database "Flat-file database") [Column-oriented](https://en.wikipedia.org/wiki/Column-oriented_DBMS "Column-oriented DBMS") [Document-oriented](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") [Object–relational](https://en.wikipedia.org/wiki/Object%E2%80%93relational_database "Object–relational database") [Deductive](https://en.wikipedia.org/wiki/Deductive_database "Deductive database") [Temporal](https://en.wikipedia.org/wiki/Temporal_database "Temporal database") [Valid time](https://en.wikipedia.org/wiki/Valid_time "Valid time") [Transaction time](https://en.wikipedia.org/wiki/Transaction_time "Transaction time") [Decision time](https://en.wikipedia.org/wiki/Decision_time "Decision time") [XML data store](https://en.wikipedia.org/wiki/XML_database "XML database") [Key–value store](https://en.wikipedia.org/wiki/Key%E2%80%93value_database "Key–value database") [Ordered Key-Value Store](https://en.wikipedia.org/wiki/Ordered_Key-Value_Store "Ordered Key-Value Store") [Triplestore](https://en.wikipedia.org/wiki/Triplestore "Triplestore") |
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Graph database
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| Readable Markdown | A **graph database** (**GDB**) is a [database](https://en.wikipedia.org/wiki/Database "Database") that uses [graph structures](https://en.wikipedia.org/wiki/Graph_\(data_structure\) "Graph (data structure)") for [semantic queries](https://en.wikipedia.org/wiki/Semantic_query "Semantic query") with [nodes](https://en.wikipedia.org/wiki/Node_\(graph_theory\) "Node (graph theory)"), [edges](https://en.wikipedia.org/wiki/Edge_\(graph_theory\) "Edge (graph theory)"), and properties to represent and store data.[\[1\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-1) A key concept of the system is the [graph](https://en.wikipedia.org/wiki/Graph_\(discrete_mathematics\) "Graph (discrete mathematics)") (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.[\[2\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:0-2)
Graph databases are commonly referred to as a [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") database. Graph databases are similar to 1970s [network model](https://en.wikipedia.org/wiki/Network_model "Network model") databases in that both represent general graphs, but network-model databases operate at a lower level of [abstraction](https://en.wikipedia.org/wiki/Abstraction_\(computer_science\) "Abstraction (computer science)")[\[3\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Gutierrez2-3) and lack easy [traversal](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal") over a chain of edges.[\[4\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-4)
The underlying storage mechanism of graph databases can vary. Relationships are first-class citizens in a graph database and can be labeled, directed, and given properties. Some depend on a relational engine and store the graph data in a [table](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)") (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 [key–value store](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair") or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "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](https://en.wikipedia.org/wiki/SQL "SQL") was for [relational databases](https://en.wikipedia.org/wiki/Relational_database "Relational database"). There is a wide variety of systems used, many of which are tightly tied to one product. Early standardization efforts led to multi-vendor query languages like [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"), [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), and [Cypher](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language"). 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)[\[5\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-5). [GQL](https://en.wikipedia.org/wiki/Graph_Query_Language "Graph Query Language") 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](https://en.wikipedia.org/wiki/Application_programming_interface "Application programming interface") (APIs).
Graph databases differ from graph compute engines. Graph databases are technologies that are translations of the relational [online transaction processing](https://en.wikipedia.org/wiki/Online_transaction_processing "Online transaction processing") (OLTP) databases. On the other hand, graph compute engines are used in [online analytical processing](https://en.wikipedia.org/wiki/Online_analytical_processing "Online analytical processing") (OLAP) for bulk analysis.[\[6\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-6) Graph databases attracted considerable attention in the 2000s, due to the successes of major technology corporations using proprietary graph databases,[\[7\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-7) along with the introduction of [open-source](https://en.wikipedia.org/wiki/Open-source_software "Open-source software") graph databases.
One study concluded that an RDBMS was "comparable" in performance to existing graph analysis engines at executing graph queries.[\[8\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-8)
In the mid-1960s, [navigational databases](https://en.wikipedia.org/wiki/Navigational_database "Navigational database") such as [IBM](https://en.wikipedia.org/wiki/IBM "IBM")'s [IMS](https://en.wikipedia.org/wiki/IBM_Information_Management_System "IBM Information Management System") supported [tree](https://en.wikipedia.org/wiki/Tree_\(data_structure\) "Tree (data structure)")\-like structures in its [hierarchical model](https://en.wikipedia.org/wiki/Hierarchical_database_model "Hierarchical database model"), but the strict [tree structure](https://en.wikipedia.org/wiki/Tree_structure "Tree structure") could be circumvented with virtual records.[\[9\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-9)[\[10\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-10)
Graph structures could be represented in network model databases from the late 1960s. [CODASYL](https://en.wikipedia.org/wiki/CODASYL "CODASYL"), which had defined [COBOL](https://en.wikipedia.org/wiki/COBOL "COBOL") in 1959, defined the Network Database Language in 1969.
[Labeled graphs](https://en.wikipedia.org/wiki/Graph_labeling "Graph labeling") could be represented in graph databases from the mid-1980s, such as the Logical Data Model.[\[11\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Gutierrez-11)[\[12\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-12)
Commercial [object databases](https://en.wikipedia.org/wiki/Object_database "Object database") (ODBMSs) emerged in the early 1990s. In 2000, the [Object Data Management Group](https://en.wikipedia.org/wiki/Object_Data_Management_Group "Object Data Management Group") published a standard language for defining object and relationship (graph) structures in their ODMG'93 publication.\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\]
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](https://en.wikipedia.org/wiki/ACID "ACID") guarantees such as [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") and [Oracle Spatial and Graph](https://en.wikipedia.org/wiki/Oracle_Spatial_and_Graph "Oracle Spatial and Graph") became available.
In the 2010s, commercial ACID graph databases that could be [scaled horizontally](https://en.wikipedia.org/wiki/Scalability#Horizontal_and_vertical_scaling "Scalability") became available. Further, [SAP HANA](https://en.wikipedia.org/wiki/SAP_HANA "SAP HANA") brought [in-memory](https://en.wikipedia.org/wiki/In-memory_database "In-memory database") and [columnar](https://en.wikipedia.org/wiki/Column-oriented_DBMS "Column-oriented DBMS") technologies to graph databases.[\[13\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-13) Also in the 2010s, [multi-model databases](https://en.wikipedia.org/wiki/Multi-model_database "Multi-model database") that supported graph models (and other models such as relational database or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database")) became available, such as [OrientDB](https://en.wikipedia.org/wiki/OrientDB "OrientDB"), [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB"), and [MarkLogic](https://en.wikipedia.org/wiki/MarkLogic "MarkLogic") (starting with its 7.0 version). During this time, graph databases of various types have become especially popular with [social network analysis](https://en.wikipedia.org/wiki/Social_network_analysis "Social network analysis") with the advent of social media companies. Also during the decade, [cloud](https://en.wikipedia.org/wiki/Cloud_computing "Cloud computing")\-based graph databases such as [Amazon Neptune](https://en.wikipedia.org/wiki/Amazon_Neptune "Amazon Neptune") and [Neo4j AuraDB](https://en.wikipedia.org/wiki/Neo4j#Licensing_and_editions "Neo4j") became available.
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](https://en.wikipedia.org/wiki/Graph_theory "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](https://en.wikipedia.org/wiki/Row_\(database\) "Row (database)") 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](https://en.wikipedia.org/wiki/Relational_model "Relational model") or a [document-store model](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database")**.**
- **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.
### Labeled-property graph
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=4 "Edit section: Labeled-property graph")\]
[](https://en.wikipedia.org/wiki/File:GraphDatabase_PropertyGraph.svg)
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 [key–value pairs](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair"). 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;[\[14\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-14) making the graph a [directed graph](https://en.wikipedia.org/wiki/Directed_graph "Directed graph"). Relationships can also have properties. This is useful in providing additional metadata and semantics to relationships of the nodes.[\[15\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-15) Direct storage of relationships allows a [constant-time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time "Time complexity") [traversal](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal").[\[16\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:32-16)
### Resource Description Framework (RDF)
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=5 "Edit section: Resource Description Framework (RDF)")\]
[](https://en.wikipedia.org/wiki/File:Rdf-graph3.png)
An example RDF graph
In an [RDF](https://en.wikipedia.org/wiki/RDF_\(computer_science\) "RDF (computer science)") 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 `hasName` 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](https://en.wikipedia.org/wiki/Literal_\(computer_programming\) "Literal (computer programming)") and/or be identified by a [URI](https://en.wikipedia.org/wiki/Uniform_Resource_Identifier "Uniform Resource Identifier"). 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 [URI](https://en.wikipedia.org/wiki/Uniform_Resource_Identifier "Uniform Resource Identifier").[\[17\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-17)
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.\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\]
The underlying storage mechanism of graph databases can vary. Some depend on a relational engine and "store" the graph data in a [table](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)") (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](https://en.wikipedia.org/wiki/Attribute%E2%80%93value_pair "Attribute–value pair") or [document-oriented database](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") for storage, making them inherently [NoSQL](https://en.wikipedia.org/wiki/NoSQL "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
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=8 "Edit section: Index-free adjacency")\]
Data lookup performance is dependent on the access speed from one particular node to another. Because [index](https://en.wikipedia.org/wiki/Database_index "Database index")\-free adjacency enforces the nodes to have direct physical [RAM](https://en.wikipedia.org/wiki/Random-access_memory "Random-access memory") 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](https://en.wikipedia.org/wiki/Cache_\(computing\) "Cache (computing)") 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 traversals](https://en.wikipedia.org/wiki/Graph_traversal "Graph traversal"). Native graph databases use index-free adjacency to process [CRUD](https://en.wikipedia.org/wiki/CRUD "CRUD") operations on the stored data.
Multiple categories of graphs by kind of data have been recognised. Gartner suggests the five broad categories of graphs:[\[18\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-18)
- [Social graph](https://en.wikipedia.org/wiki/Social_graph "Social graph"): this is about the connections between people; examples include [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook"), [Twitter](https://en.wikipedia.org/wiki/Twitter "Twitter"), and the idea of [six degrees of separation](https://en.wikipedia.org/wiki/Six_degrees_of_separation "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](https://en.wikipedia.org/wiki/Interest_graph "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](https://en.wikipedia.org/wiki/Internet_of_things "Internet of things") (IoT) devices.
## Comparison with relational databases
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=10 "Edit section: Comparison with relational databases")\]
Since [Edgar F. Codd](https://en.wikipedia.org/wiki/Edgar_F._Codd "Edgar F. Codd")'s 1970 paper on the [relational model](https://en.wikipedia.org/wiki/Relational_model "Relational model"),[\[19\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:2-19) [relational databases](https://en.wikipedia.org/wiki/Relational_databases "Relational databases") have been the de facto industry standard for large-scale data storage systems. Relational models require a strict schema and [data normalization](https://en.wikipedia.org/wiki/Data_normalization "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](https://en.wikipedia.org/wiki/Data_consistency "Data consistency") and support [ACID transactions](https://en.wikipedia.org/wiki/ACID_\(computer_science\) "ACID (computer science)"). 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.[\[19\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:2-19) 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](https://en.wikipedia.org/wiki/Foreign_key "Foreign key") constraints should also be considered when retrieving relationships, causing additional overhead.
Compared with [relational databases](https://en.wikipedia.org/wiki/Relational_database "Relational database"), graph databases are often faster for associative data sets[\[20\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-20) and map more directly to the structure of [object-oriented](https://en.wikipedia.org/wiki/Object-oriented_programming "Object-oriented programming") applications. They can scale more naturally[\[21\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-21) to large datasets as they do not typically need [join](https://en.wikipedia.org/wiki/Join_\(SQL\) "Join (SQL)") 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 [\[22\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-22) 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.[\[23\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:12-23)
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](https://en.wikipedia.org/wiki/Table_\(database\) "Table (database)"), 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 [indexes](https://en.wikipedia.org/wiki/Database_index "Database index"), which allow data to be stored in a smaller sub-table, containing only the selected data and a [unique key](https://en.wikipedia.org/wiki/Unique_key "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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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](https://en.wikipedia.org/wiki/Primary_key "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](https://en.wikipedia.org/wiki/Join_\(SQL\) "Join (SQL)"), 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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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 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.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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 [backlinks](https://en.wikipedia.org/wiki/Backlink "Backlink") 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](https://en.wikipedia.org/wiki/Big_O_notation "Big O notation"), this query would be  time – i.e., proportional to the logarithm of the size of the data. In contrast, the relational version would be multiple  lookups, plus the  time needed to join all of the data records.[\[24\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-from-24)
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](https://en.wikipedia.org/wiki/Gone_with_the_Wind_\(film\) "Gone with the Wind (film)")*". 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](https://en.wikipedia.org/wiki/Clark_Gable "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.[\[25\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-examples-25)
*Properties* add another layer of [abstraction](https://en.wikipedia.org/wiki/Abstraction_\(computer_science\) "Abstraction (computer science)") 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](https://en.wikipedia.org/wiki/Vivien_Leigh "Vivien Leigh"), [Olivia de Havilland](https://en.wikipedia.org/wiki/Olivia_de_Havilland "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.[\[25\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-examples-25)
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](https://en.wikipedia.org/wiki/Social_networking "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](https://en.wikipedia.org/wiki/Big_data "Big data") environments. For this reason, graph databases are becoming very popular for large online systems like [Facebook](https://en.wikipedia.org/wiki/Facebook "Facebook"), [Google](https://en.wikipedia.org/wiki/Google "Google"), [Twitter](https://en.wikipedia.org/wiki/Twitter "Twitter"), and similar systems with deep links between records.
To further illustrate, imagine a relational model with two tables: a `people` table (which has a `person_id` and `person_name` column) and a `friend` table (with `friend_id` and `person_id`, which is a [foreign key](https://en.wikipedia.org/wiki/Foreign_key "Foreign key") from the `people` table). In this case, searching for all of Jack's friends would result in the following SQL query.
```
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';
```
The same query may be translated into --
- [Cypher](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language"), a graph database [query language](https://en.wikipedia.org/wiki/Query_language "Query language")
```
MATCH (p1:person {name: 'Jack'})-[:FRIEND_WITH]-(p2:person)
RETURN p2.name
```
- [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(query_language\) "Gremlin (query language)"), an imperative style graph [query language](https://en.wikipedia.org/wiki/Query_language "Query language") maintained by Apache TinkerPop and used by many graph databases[\[26\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-26)
```
g.V().hasLabel("person").has("name", "Jack").
out("friendsWith").
hasLabel("person").
values("name")
```
- [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), an RDF graph database [query language](https://en.wikipedia.org/wiki/Query_language "Query language") standardized by [W3C](https://en.wikipedia.org/wiki/W3C "W3C") and used in multiple RDF [Triple](https://en.wikipedia.org/wiki/Triplestore "Triplestore") and [Quad](https://en.wikipedia.org/wiki/Named_graph "Named graph") stores
- Long form
```
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 .
}
```
- Short form
```
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
SELECT ?name
WHERE { ?s foaf:name "Jack" ;
foaf:knows ?o .
?o foaf:name ?name .
}
```
- SPASQL, a hybrid database query language, that extends [SQL](https://en.wikipedia.org/wiki/SQL "SQL") with [SPARQL](https://en.wikipedia.org/wiki/SPARQL "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 ;
```
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.[\[16\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-:32-16)
## List of graph databases
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=12 "Edit section: List of graph databases")\]
The following is a list of [notable](https://en.wikipedia.org/wiki/Wikipedia:GNG "Wikipedia:GNG") graph databases:
| Name | Current version | Latest release date (YYYY-MM-DD) | [Software license](https://en.wikipedia.org/wiki/Software_license "Software license") | [Programming language](https://en.wikipedia.org/wiki/Programming_language "Programming language") | Description |
|---|---|---|---|---|---|
| [Aerospike](https://en.wikipedia.org/wiki/Aerospike_\(database\) "Aerospike (database)") | 7\.0 | 2024-05-15 | 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](https://en.wikipedia.org/w/index.php?title=AgensGraph&action=edit&redlink=1 "AgensGraph (page does not exist)")[\[27\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-27) | 2\.16.0 | 2025-09-12[\[28\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-28) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") Community version, [proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") 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](https://en.wikipedia.org/wiki/PostgreSQL "PostgreSQL") RDBMS, providing a highly reliable, fully-featured platform ready for enterprise use. |
| [AllegroGraph](https://en.wikipedia.org/wiki/AllegroGraph "AllegroGraph") | 7\.0.0 | 2022-12-20 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), clients: [Eclipse Public License](https://en.wikipedia.org/wiki/Eclipse_Public_License "Eclipse Public License") v1 | [C\#](https://en.wikipedia.org/wiki/C_Sharp_\(programming_language\) "C Sharp (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [Common Lisp](https://en.wikipedia.org/wiki/Common_Lisp "Common Lisp"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | [Resource Description Framework](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework") (RDF) and graph database. |
| [Amazon Neptune](https://en.wikipedia.org/wiki/Amazon_Neptune "Amazon Neptune") | 1\.4.7.0 | 2026-03-03[\[29\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-29) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | Not disclosed | Amazon Neptune is a fully managed graph database by [Amazon.com](https://en.wikipedia.org/wiki/Amazon.com "Amazon.com"). It is used as a [web service](https://en.wikipedia.org/wiki/Web_service "Web service"), and is part of [Amazon Web Services](https://en.wikipedia.org/wiki/Amazon_Web_Services "Amazon Web Services"). Supports popular graph models property graph and [W3C](https://en.wikipedia.org/wiki/W3C "W3C")'s [RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework"), and their respective [query languages](https://en.wikipedia.org/wiki/Query_language "Query language") Apache TinkerPop, [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"), [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), and [openCypher](https://en.wikipedia.org/wiki/Cypher_\(query_language\) "Cypher (query language)"). |
| [Altair Graph Studio](https://en.wikipedia.org/wiki/Altair_Engineering "Altair Engineering") | 6\.3 | 2025-12 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | AnzoGraph DB is a [massively parallel](https://en.wikipedia.org/wiki/Massively_parallel "Massively parallel") native Graph Online Analytics Processing ([GOLAP](https://en.wikipedia.org/w/index.php?title=GOLAP&action=edit&redlink=1 "GOLAP (page does not exist)")) style database built to support [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL") and [Cypher Query Language](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language") to analyze trillions of relationships. AnzoGraph DB is designed for interactive analysis of large sets of [semantic triple](https://en.wikipedia.org/wiki/Semantic_triple "Semantic triple") data, but also supports labeled properties under proposed [W3C](https://en.wikipedia.org/wiki/W3C "W3C") standards.[\[30\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-30)[\[31\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-31)[\[32\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-32)[\[33\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-33) |
| [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB") | 3\.12.4.2 | 2025-04-09 | [Free](https://en.wikipedia.org/wiki/Free_software "Free software") [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License"), [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript"), [.NET](https://en.wikipedia.org/wiki/.NET ".NET"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [Node.js](https://en.wikipedia.org/wiki/Node.js "Node.js"), [PHP](https://en.wikipedia.org/wiki/PHP "PHP"), [Scala](https://en.wikipedia.org/wiki/Scala_\(programming_language\) "Scala (programming language)"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Ruby](https://en.wikipedia.org/wiki/Ruby_\(programming_language\) "Ruby (programming language)"), [Elixir](https://en.wikipedia.org/wiki/Elixir_\(programming_language\) "Elixir (programming language)") | [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") native graph database system developed by ArangoDB Inc, supporting three data models (key/value, documents, graphs, vector), 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](https://en.wikipedia.org/wiki/Cosmos_DB "Cosmos DB") | | 2017 | Proprietary | Not disclosed | Multi-modal database which supports graph concepts using the [Apache Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(query_language\) "Gremlin (query language)") query language |
| [DataStax](https://en.wikipedia.org/wiki/DataStax "DataStax") Enterprise Graph | v6.0.1 | 2018-06 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Distributed, real-time, scalable database; supports Tinkerpop, and integrates with [Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra "Apache Cassandra")[\[34\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-34) |
| [FalkorDB](https://github.com/FalkorDB/FalkorDB) | 4\.16 | 2026-01 | [SSPLv1](https://en.wikipedia.org/wiki/Server_Side_Public_License "Server Side Public License") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [Rust](https://en.wikipedia.org/wiki/Rust_\(programming_language\) "Rust (programming language)") | High-performance, in-memory graph database that uses sparse matrix multiplication ([GraphBLAS](https://en.wikipedia.org/wiki/GraphBLAS "GraphBLAS")) for query execution; it is the community-led successor to [RedisGraph](https://en.wikipedia.org/wiki/Redis "Redis") following its end-of-life; optimized for low-latency [GraphRAG](https://en.wikipedia.org/wiki/Retrieval-augmented_generation "Retrieval-augmented generation") and AI applications. |
| [Graph in Microsoft Fabric](https://learn.microsoft.com/fabric/graph/overview) | | 2025-10 | Proprietary | Not disclosed | Microsoft's first native, horizontally scalable graph data platform that integrates data management, analytics, and visualization. It uses [GQL](https://en.wikipedia.org/wiki/Graph_Query_Language "Graph Query Language") as the query language. |
| [Google Spanner Graph](https://en.wikipedia.org/wiki/Google_Spanner "Google Spanner") | N/A | 2025-01 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | A horizontally scalable, multi-model database that provides a native graph experience on top of [Spanner](https://en.wikipedia.org/wiki/Spanner_\(database\) "Spanner (database)"); supports the [ISO](https://en.wikipedia.org/wiki/ISO "ISO") [GQL](https://en.wikipedia.org/w/index.php?title=GQL_\(database_query_language\)&action=edit&redlink=1 "GQL (database query language) (page does not exist)") standard and [openCypher](https://en.wikipedia.org/wiki/Cypher_\(query_language\) "Cypher (query language)") for matching patterns and traversing relationships; designed for global consistency and trillions of edges. |
| [GUN (Graph Universe Node)](https://en.wikipedia.org/wiki/GUN_\(graph_database\) "GUN (graph database)") | 0\.2020.1240 | 2024 | Open source, [MIT License](https://en.wikipedia.org/wiki/MIT_License "MIT License"), [Apache 2.0](https://en.wikipedia.org/wiki/Apache_License "Apache License"), [zlib License](https://en.wikipedia.org/wiki/Zlib_License "Zlib License") | [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") | An [open source](https://en.wikipedia.org/wiki/Open_source "Open source"), [offline-first](https://en.wikipedia.org/wiki/Online_and_offline "Online and offline"), [real-time](https://en.wikipedia.org/wiki/Real-time_communication "Real-time communication"), [decentralized](https://en.wikipedia.org/wiki/Decentralized_web "Decentralized web"), graph database written in [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") for the [web browser](https://en.wikipedia.org/wiki/Web_browser "Web browser").[\[35\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-35)[\[36\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-36) It is implemented as a [peer-to-peer](https://en.wikipedia.org/wiki/Peer-to-peer "Peer-to-peer") network featuring [multi-master replication](https://en.wikipedia.org/wiki/Multi-master_replication "Multi-master replication") with a custom [commutative replicated data type (CRDT)](https://en.wikipedia.org/wiki/Conflict-free_replicated_data_type "Conflict-free replicated data type").\[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed "Wikipedia:Citation needed")*\] |
| [InfiniteGraph](https://en.wikipedia.org/wiki/InfiniteGraph "InfiniteGraph") | 2021\.2 | 2021-05 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [commercial, free 50GB version](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/JanusGraph "JanusGraph") | 1\.1.0 | 2024-11-07[\[37\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-37) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Open source, scalable, distributed across a multi-machine cluster graph database under The [Linux Foundation](https://en.wikipedia.org/wiki/Linux_Foundation "Linux Foundation"); supports various storage backends ([Apache Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra "Apache Cassandra"), [Apache HBase](https://en.wikipedia.org/wiki/Apache_HBase "Apache HBase"), Google Cloud [Bigtable](https://en.wikipedia.org/wiki/Bigtable "Bigtable"), Oracle [Berkeley DB](https://en.wikipedia.org/wiki/Berkeley_DB "Berkeley DB"));[\[38\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-38) supports global graph data analytics, reporting, and [extract, transform, load](https://en.wikipedia.org/wiki/Extract,_transform,_load "Extract, transform, load") (ETL) through integration with big data platforms ([Apache Spark](https://en.wikipedia.org/wiki/Apache_Spark "Apache Spark"), [Apache Giraph](https://en.wikipedia.org/wiki/Apache_Giraph "Apache Giraph"), [Apache Hadoop](https://en.wikipedia.org/wiki/Apache_Hadoop "Apache Hadoop")); supports geo, numeric range, and full-text search via external index storages ([Elasticsearch](https://en.wikipedia.org/wiki/Elasticsearch "Elasticsearch"), [Apache Solr](https://en.wikipedia.org/wiki/Apache_Solr "Apache Solr"), [Apache Lucene](https://en.wikipedia.org/wiki/Apache_Lucene "Apache Lucene")).[\[39\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-39) |
| [Kùzu](https://github.com/kuzudb/kuzu) | 0\.11.3 | 2025-10 | [MIT](https://en.wikipedia.org/wiki/MIT_License "MIT License") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | Embeddable, open-source graph database management system (GDBMS) featuring a vectorized query engine; the project was abandoned by its creator and sponsor Kùzu Inc. in October 2025, with the repository archived and documentation moved to GitHub.[\[40\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-40) |
| [Ladybug](https://github.com/LadybugDB/ladybug) | 0\.15.1 | 2026-03 | [MIT](https://en.wikipedia.org/wiki/MIT_License "MIT License") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | Embedded columnar graph database, based on a fork of Kùzu. Supports querying Apache Parquet and Apache Arrow with zero copy.[\[41\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-41) |
| [MarkLogic](https://en.wikipedia.org/wiki/MarkLogic "MarkLogic") | 8\.0.4 | 2015 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") developer version | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Multi-model [NoSQL](https://en.wikipedia.org/wiki/NoSQL "NoSQL") database that stores [documents](https://en.wikipedia.org/wiki/Document-oriented_database "Document-oriented database") (JSON and XML) and semantic graph data ([RDF](https://en.wikipedia.org/wiki/Resource_Description_Framework "Resource Description Framework") triples); also has a built-in search engine. |
| [Microsoft SQL Server](https://en.wikipedia.org/wiki/Microsoft_SQL_Server "Microsoft SQL Server") 2017 | RC1 | | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [SQL](https://en.wikipedia.org/wiki/SQL "SQL")/T-SQL, [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | 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.[\[42\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-42) |
| [NebulaGraph](https://en.wikipedia.org/wiki/NebulaGraph "NebulaGraph") | 3\.8.0 | 2024-05 | Open Source Edition is under Apache 2.0, Common Clause 1.0 | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)") | 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.[\[43\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-43) |
| [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") | 2026\.03.1 | 2026-04-02[\[44\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-44) | [GPLv3](https://en.wikipedia.org/wiki/GNU_General_Public_License "GNU General Public License") Community Edition, [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") and [AGPLv3](https://en.wikipedia.org/wiki/Affero_General_Public_License "Affero General Public License") options for enterprise and advanced editions | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [.NET](https://en.wikipedia.org/wiki/.NET ".NET"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [Go](https://en.wikipedia.org/wiki/Go_\(programming_language\) "Go (programming language)"), [Ruby](https://en.wikipedia.org/wiki/Ruby_\(programming_language\) "Ruby (programming language)"), [PHP](https://en.wikipedia.org/wiki/PHP "PHP"), [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)"), [Erlang](https://en.wikipedia.org/wiki/Erlang_\(programming_language\) "Erlang (programming language)")/[Elixir](https://en.wikipedia.org/wiki/Elixir_\(programming_language\) "Elixir (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)")/[C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Clojure](https://en.wikipedia.org/wiki/Clojure "Clojure"), [Perl](https://en.wikipedia.org/wiki/Perl "Perl"), [Haskell](https://en.wikipedia.org/wiki/Haskell "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 [REST](https://en.wikipedia.org/wiki/Representational_state_transfer "Representational state transfer") [web API](https://en.wikipedia.org/wiki/Web_API "Web API") interface, and a proprietary Bolt protocol with official drivers. |
| [Ontotext GraphDB](https://en.wikipedia.org/wiki/Ontotext_GraphDB "Ontotext GraphDB") | 11\.3.1 | 2026-02-19[\[45\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-45) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), Standard and Enterprise Editions are [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software"), Free Edition is [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Highly efficient and robust semantic graph database with RDF and SPARQL support, also available as a high-availability cluster. Integrates [OpenRefine](https://en.wikipedia.org/wiki/OpenRefine "OpenRefine") for ingestion and reconciliation of tabular data and [ontop](https://en.wikipedia.org/wiki/Ontop "Ontop") for [Ontology-Based Data Access](https://en.wikipedia.org/w/index.php?title=Ontology-Based_Data_Access&action=edit&redlink=1 "Ontology-Based Data Access (page does not exist)"). Connects to [Lucene](https://en.wikipedia.org/wiki/Apache_Lucene "Apache Lucene"), [SOLR](https://en.wikipedia.org/wiki/Apache_Solr "Apache Solr") and [Elasticsearch](https://en.wikipedia.org/wiki/Elasticsearch "Elasticsearch") for [Full text](https://en.wikipedia.org/wiki/Full_text_search "Full text search") and [Faceted search](https://en.wikipedia.org/wiki/Faceted_search "Faceted search"), and [Kafka](https://en.wikipedia.org/wiki/Apache_Kafka "Apache Kafka") for event and stream processing. Supports [OGC](https://en.wikipedia.org/wiki/Open_Geospatial_Consortium "Open Geospatial Consortium") [GeoSPARQL](https://en.wikipedia.org/wiki/GeoSPARQL "GeoSPARQL"). Provides [JDBC](https://en.wikipedia.org/wiki/Java_Database_Connectivity "Java Database Connectivity") access to [Knowledge Graphs](https://en.wikipedia.org/wiki/Knowledge_graph "Knowledge graph").[\[46\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-46) |
| OpenLink [Virtuoso](https://en.wikipedia.org/wiki/Virtuoso_Universal_Server "Virtuoso Universal Server") | 8\.2 | 2018-10 | Open Source Edition is [GPLv2](https://en.wikipedia.org/wiki/GNU_General_Public_License "GNU General Public License"), Enterprise Edition is [proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/NEPOMUK_\(software\) "NEPOMUK (software)") Semantic Desktop), a one-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server[\[47\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Virtuoso_Clustering_Diagrams-47) |
| Oracle RDF Graph; part of [Oracle Database](https://en.wikipedia.org/wiki/Oracle_Database "Oracle Database") | 21c | 2020 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"), [SQL](https://en.wikipedia.org/wiki/SQL "SQL") | RDF Graph capabilities as features in multi-model Oracle Database: RDF Graph: comprehensive [W3C](https://en.wikipedia.org/wiki/W3C "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 | [PGQL](https://en.wikipedia.org/wiki/Graph_Query_Language#PGQL "Graph Query Language"), 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](https://en.wikipedia.org/wiki/SQL "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](https://en.wikipedia.org/wiki/OrientDB "OrientDB") | 3\.2.28 | 2024-02 | Community Edition is [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License"), Enterprise Edition is [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | Second-generation[\[48\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-48) 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](https://en.wikipedia.org/wiki/ACID "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](https://en.wikipedia.org/wiki/SQL "SQL"). It has HTTP [REST](https://en.wikipedia.org/wiki/Representational_state_transfer "Representational state transfer") and [JSON](https://en.wikipedia.org/wiki/JSON "JSON") [API](https://en.wikipedia.org/wiki/API "API"). |
| [RedisGraph](https://en.wikipedia.org/wiki/Redis_Labs "Redis Labs") | 2\.0.20 | 2020-09 | Redis Source Available License,[AGPLv3](https://en.wikipedia.org/wiki/Affero_General_Public_License "Affero General Public License"),[SSPL](https://en.wikipedia.org/wiki/Server_Side_Public_License "Server Side Public License") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)") | In-memory, queryable Property Graph database which uses [sparse matrices](https://en.wikipedia.org/wiki/Sparse_matrix "Sparse matrix") to represent the [adjacency matrix](https://en.wikipedia.org/wiki/Adjacency_matrix "Adjacency matrix") in graphs and [linear algebra](https://en.wikipedia.org/wiki/Linear_algebra "Linear algebra") to query the graph.[\[49\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-49) |
| [SAP HANA](https://en.wikipedia.org/wiki/SAP_HANA "SAP HANA") | 2\.0 SPS 05 | 2020-06[\[50\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-50) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") and [SQL](https://en.wikipedia.org/wiki/SQL "SQL")\-like language | In-memory [ACID](https://en.wikipedia.org/wiki/ACID "ACID") transaction supported property graph[\[51\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-SapHana-51) |
| [Sparksee](https://en.wikipedia.org/wiki/Sparksee_\(graph_database\) "Sparksee (graph database)") | 5\.2.0 | 2015 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software"), [commercial](https://en.wikipedia.org/wiki/Commercial_software "Commercial software"), [freeware](https://en.wikipedia.org/wiki/Freeware "Freeware") for evaluation, research, development | [C++](https://en.wikipedia.org/wiki/C%2B%2B "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](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), C++, [C\#](https://en.wikipedia.org/wiki/C_Sharp_\(programming_language\) "C Sharp (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), and [Objective-C](https://en.wikipedia.org/wiki/Objective-C "Objective-C"); version 5 is the first graph [mobile database](https://en.wikipedia.org/wiki/Mobile_database "Mobile database"). |
| [Teradata Aster](https://en.wikipedia.org/wiki/Teradata#Aster_Platform "Teradata") | 7 | 2016 | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [SQL](https://en.wikipedia.org/wiki/SQL "SQL"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++"), [R](https://en.wikipedia.org/wiki/R_\(programming_language\) "R (programming language)") | [Massive parallel processing](https://en.wikipedia.org/wiki/Massive_parallel_processing "Massive parallel processing") (MPP) database incorporating patented engines supporting native SQL, [MapReduce](https://en.wikipedia.org/wiki/MapReduce "MapReduce"), and graph data storage and manipulation; provides a set of analytic function libraries and data visualization[\[52\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-52) |
| [TerminusDB](https://en.wikipedia.org/wiki/TerminusDB "TerminusDB") | 11\.0.6 | 2023-05-03[\[53\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-53) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Prolog](https://en.wikipedia.org/wiki/Prolog "Prolog"), [Rust](https://en.wikipedia.org/wiki/Rust_\(programming_language\) "Rust (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [JSON-LD](https://en.wikipedia.org/wiki/JSON-LD "JSON-LD") | Document-oriented knowledge graph; the power of an enterprise knowledge graph with the simplicity of documents. |
| [TigerGraph](https://en.wikipedia.org/wiki/TigerGraph "TigerGraph") | 4\.1.2 | 2024-12-20[\[54\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-54) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [C++](https://en.wikipedia.org/wiki/C%2B%2B "C++") | [Massive parallel processing](https://en.wikipedia.org/wiki/Massive_parallel_processing "Massive parallel processing") (MPP) native graph database management system[\[55\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-Forrester-55) |
| TinkerGraph | 3\.8.0 | 2025-11-12[\[56\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-56) | [Apache 2](https://en.wikipedia.org/wiki/Apache_License#Version_2.0 "Apache License") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)") | TinkerGraph is a single machine, in-memory (with optional persistence), graph engine that provides both OLTP and OLAP functionality. TinkerGraph is deployed with Apache TinkerPop and serves as the reference implementation for other providers to study in order to understand the semantics of the various methods of the TinkerPop API.[\[57\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-57) |
| [TypeDB](https://en.wikipedia.org/wiki/GRAKN.AI "GRAKN.AI") | 2\.14.0 | 2022-11[\[58\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-58) | Free, [GNU AGPLv3](https://en.wikipedia.org/wiki/GNU_Affero_General_Public_License "GNU Affero General Public License"), [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Java](https://en.wikipedia.org/wiki/Java_\(programming_language\) "Java (programming language)"), [Python](https://en.wikipedia.org/wiki/Python_\(programming_language\) "Python (programming language)"), [JavaScript](https://en.wikipedia.org/wiki/JavaScript "JavaScript") | TypeDB is a strongly-typed database software with an extensible type system. |
| Tarantool Graph DB | 1\.2.0 | 2024-01-01[\[59\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-59) | [Proprietary](https://en.wikipedia.org/wiki/Proprietary_software "Proprietary software") | [Lua](https://en.wikipedia.org/wiki/Lua_\(programming_language\) "Lua (programming language)"), [C](https://en.wikipedia.org/wiki/C_\(programming_language\) "C (programming language)") | Tarantool Graph DB is a graph-vector database. Analyze data connections in real time using a high-speed graph and vector storage |
## Graph query-programming languages
\[[edit](https://en.wikipedia.org/w/index.php?title=Graph_database&action=edit§ion=13 "Edit section: Graph query-programming languages")\]
- [AQL (ArangoDB Query Language)](https://en.wikipedia.org/wiki/AQL_\(ArangoDB_Query_Language\) "AQL (ArangoDB Query Language)"): a SQL-like query language used in [ArangoDB](https://en.wikipedia.org/wiki/ArangoDB "ArangoDB") for both documents and graphs
- [Cypher Query Language](https://en.wikipedia.org/wiki/Cypher_Query_Language "Cypher Query Language") (Cypher): a graph query [declarative language](https://en.wikipedia.org/wiki/Declarative_language "Declarative language") for [Neo4j](https://en.wikipedia.org/wiki/Neo4j "Neo4j") that enables ad hoc and programmatic (SQL-like) access to the graph.[\[60\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-60)
- [GQL](https://en.wikipedia.org/wiki/GQL_Graph_Query_Language "GQL Graph Query Language"): proposed ISO standard graph query language
- [Gremlin](https://en.wikipedia.org/wiki/Gremlin_\(programming_language\) "Gremlin (programming language)"): a graph programming language that is a part of Apache TinkerPop open-source project[\[61\]](https://en.wikipedia.org/wiki/Graph_database#cite_note-61)
- [SPARQL](https://en.wikipedia.org/wiki/SPARQL "SPARQL"): a query language for RDF databases that can retrieve and manipulate data stored in RDF format
- [regular path queries](https://en.wikipedia.org/wiki/Regular_path_query "Regular path query"), a theoretical language for queries on graph databases
- [Graph transformation](https://en.wikipedia.org/wiki/Graph_transformation "Graph transformation") – Creating a new graph from an existing graph
- [Hierarchical database model](https://en.wikipedia.org/wiki/Hierarchical_database_model "Hierarchical database model") – Tree-like structure for data
- [Datalog](https://en.wikipedia.org/wiki/Datalog "Datalog") – Declarative logic programming language
- [Vadalog](https://en.wikipedia.org/wiki/Vadalog "Vadalog") – Type of Knowledge Graph Management System
- [Object database](https://en.wikipedia.org/wiki/Object_database "Object database") – Database presenting data as objects
- [RDF Database](https://en.wikipedia.org/wiki/RDF_Database "RDF Database") – Database for storage and retrieval of triples
- [Structured storage](https://en.wikipedia.org/wiki/Structured_storage "Structured storage") – Database class for storage and retrieval of modeled data
- [Text graph](https://en.wikipedia.org/wiki/Text_graph "Text graph") – Text-structure representation using graph models
- [Vector database](https://en.wikipedia.org/wiki/Vector_database "Vector database") – Type of database that uses vectors to represent other data
- [Wikidata](https://en.wikipedia.org/wiki/Wikidata "Wikidata") – Collaborative multilingual knowledge 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](https://en.wikipedia.org/wiki/SPARQL "SPARQL") queries.
1. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-1)**
Bourbakis, Nikolaos G. (1998). [*Artificial Intelligence and Automation*](https://books.google.com/books?id=mV3wxKLHlnwC&q=%22gdb%22+%22graph+database%22&pg=PA381). World Scientific. p. 381. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9789810226374](https://en.wikipedia.org/wiki/Special:BookSources/9789810226374 "Special:BookSources/9789810226374")
. Retrieved 2018-04-20.
2. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-:0_2-0)**
Yoon, Byoung-Ha; Kim, Seon-Kyu; Kim, Seon-Young (March 2017). ["Use of Graph Database for the Integration of Heterogeneous Biological Data"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389944). *Genomics & Informatics*. **15** (1): 19–27\. [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10\.5808/GI.2017.15.1.19](https://doi.org/10.5808%2FGI.2017.15.1.19). [ISSN](https://en.wikipedia.org/wiki/ISSN_\(identifier\) "ISSN (identifier)") [1598-866X](https://search.worldcat.org/issn/1598-866X). [PMC](https://en.wikipedia.org/wiki/PMC_\(identifier\) "PMC (identifier)") [5389944](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5389944). [PMID](https://en.wikipedia.org/wiki/PMID_\(identifier\) "PMID (identifier)") [28416946](https://pubmed.ncbi.nlm.nih.gov/28416946).
3. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-Gutierrez2_3-0)**
Angles, Renzo; Gutierrez, Claudio (1 Feb 2008). ["Survey of graph database models"](https://web.archive.org/web/20170815064527/https://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf) (PDF). *ACM Computing Surveys*. **40** (1): 1–39\. [CiteSeerX](https://en.wikipedia.org/wiki/CiteSeerX_\(identifier\) "CiteSeerX (identifier)") [10\.1.1.110.1072](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.1072). [doi](https://en.wikipedia.org/wiki/Doi_\(identifier\) "Doi (identifier)"):[10\.1145/1322432.1322433](https://doi.org/10.1145%2F1322432.1322433). [S2CID](https://en.wikipedia.org/wiki/S2CID_\(identifier\) "S2CID (identifier)") [207166126](https://api.semanticscholar.org/CorpusID:207166126). Archived from [the original](http://www.cse.iitk.ac.in/users/smitr/PhD%20Resources/Survey%20of%20Graph%20Databases%20Models.pdf) (PDF) on 15 August 2017. Retrieved 28 May 2016. "network models \[...\] lack a good abstraction level: it is difficult to separate the db-model from the actual implementation"
4. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-4)**
Silberschatz, Avi (28 January 2010). [*Database System Concepts, Sixth Edition*](http://codex.cs.yale.edu/avi/db-book/db6/appendices-dir/d.pdf) (PDF). McGraw-Hill. p. D-29. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[978-0-07-352332-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-07-352332-3 "Special:BookSources/978-0-07-352332-3")
.
5. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-5)**
Walter, Lance (2019-12-02). ["The Graph Market Is Standardizing and That's Great News for Users"](https://www.rtinsights.com/iso-graph-query-language-standard/). *RTInsights*. Retrieved 2026-03-27.
6. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-6)**
Robinson, Ian (2015-06-10). [*Graph Databases: New Opportunities for Connected Data*](https://books.google.com/books?id=RTvcCQAAQBAJ&pg=PA4). O'Reilly Media, Inc. p. 4. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[9781491930861](https://en.wikipedia.org/wiki/Special:BookSources/9781491930861 "Special:BookSources/9781491930861")
.
7. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-7)**
["Graph Databases Burst into the Mainstream"](https://www.kdnuggets.com/2018/02/graph-databases-burst-into-the-mainstream.html). *www.kdnuggets.com*. Retrieved 2018-10-23.
8. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-8)**
Fan, Jing; Gerald, Adalbert (2014-12-25). [*The case against specialized graph analytics engines*](http://cidrdb.org/cidr2015/Papers/CIDR15_Paper20.pdf) (PDF). Conference on Innovative Data Systems Research (CIDR).
9. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-9)**
Silberschatz, Avi (28 January 2010). [*Database System Concepts, Sixth Edition*](http://codex.cs.yale.edu/avi/db-book/db6/appendices-dir/e.pdf) (PDF). McGraw-Hill. p. E-20. [ISBN](https://en.wikipedia.org/wiki/ISBN_\(identifier\) "ISBN (identifier)")
[978-0-07-352332-3](https://en.wikipedia.org/wiki/Special:BookSources/978-0-07-352332-3 "Special:BookSources/978-0-07-352332-3")
.
10. **[^](https://en.wikipedia.org/wiki/Graph_database#cite_ref-10)**
Parker, Lorraine. ["IMS Notes"](http://www.people.vcu.edu/~lparker/IMS.html). *vcu.edu*. Retrieved 31 May 2016.
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| Root Hash | 17790707453426894952 |
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