In computing, a graph database is a database that uses graph structures with nodes, edges, and properties to represent and store data. A graph database is any storage system that provides index-free adjacency. This means that every element contains a direct pointer to its adjacent elements and no index lookups are necessary. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases.
Graph databases are based on graph theory. Graph databases employ nodes, properties, and edges.
Nodes represent entities such as people, businesses, accounts, or any other item you might want to keep track of.
Properties are pertinent information that relate to nodes. For instance, if "Wikipedia" were one of the nodes, one might have it tied to properties such as "website", "reference material", or "word that starts with the letter 'w'", depending on which aspects of "Wikipedia" are pertinent to the particular database.
Edges are the lines that connect nodes to nodes or nodes to properties and they represent the relationship between the two. Most of the important information is really stored in the edges. Meaningful patterns emerge when one examines the connections and interconnections of nodes, properties, and edges.
Compared with relational databases, graph databases are often faster for associative data sets, and map more directly to the structure of object-oriented applications. They can scale more naturally to large data sets as they do not typically require expensive join operations. As they depend less on a rigid schema, they are more suitable to manage ad hoc and changing data with evolving schemas. Conversely, relational databases are typically faster at performing the same operation on large numbers of data elements.
Graph databases are a powerful tool for graph-like queries, for example computing the shortest path between two nodes in the graph. Other graph-like queries can be performed over a graph database in a natural way (for example graph's diameter computations or community detection).
The following is a list of several well-known graph database projects:
|AllegroGraph||4.14.1 (September 2014)||Proprietary, Clients - Eclipse Public License v1||C#, C, Common Lisp, Java, Python||A RDF and graph database.|
|Bigdata||1.3.1 (May 2014)||GPLv2, evaluation license, or commercial license.||Java||A RDF/graph database capable of clustered deployment. Bigdata supports high availability (HA) mode, embedded mode, single server mode. As of version 1.3.1, it supports the Blueprints API and Reification Done Right (RDR).|
|Bitsy||1.5.0||AGPL, Enterprise license (unlimited use, annual/perpetual)||Java||A small, embeddable, durable in-memory graph database|
|BrightstarDB||MIT License ||C#||An embeddable NoSQL database for the .NET platform with code-first data model generation.|
|DEX/Sparksee||5.0.0 (2014)||evaluation, research or development use (free) / commercial use||C++||A high-performance and scalable graph database management system from Sparsity Technologies, a technology transition company from DAMA-UPC. Its main characteristics is its query performance for the retrieval & exploration of large networks. Sparksee 5 mobile is the first graph database for mobile devices.|
|Filament||BSD||Java||A graph persistence framework and associated toolkits based on a navigational query style.|
|GraphBase||1.0.03a||Proprietary||Java||A customizable, distributed, small-footprint graph store with a rich tool set from FactNexus.|
|Graphd||Proprietary||The proprietary back-end of Freebase.|
|Horton||Proprietary||C#||A graph database from Microsoft Research Extreme Computing Group (XCG) based on the cloud programming infrastructure Orleans.|
|HyperGraphDB||1.2 (2012)||LGPL||Java||A graph database supporting generalized hypergraphs where edges can point to other edges.|
|IBM System G Native Store||v1.0 (July 2014)||Proprietary||C, C++, Java||A high performance graph store using natively implemented graph data structures and primitives for achieving superior efficiency. IBM System G Native Store can handle various simple graphs, property graphs, and RDF graphs, in terms of storage, analytics, and visualization. Native Store is accessible from most programming languages by providing APIs in C++, Java (Tinkerpop/Blueprints), and Python. Its gShell graph command collection and the Native Store REST APIs provide language-free interfaces.|
|InfiniteGraph||3.0 (January 2013)||Proprietary||Java||A distributed and cloud-enabled commercial product with flexible licensing.|
|InfoGrid||2.9.5 (2011)||AGPLv3, free for small entities||Java||A graph database with web front end and configurable storage engines (MySQL, PostgreSQL, Files, Hadoop).|
|jCoreDB Graph||An extensible database engine with a graph database subproject.|
|MapGraph||v3 (2014)||Apache 2||CUDA||MapGraph is Massively Parallel Graph processing on GPUs. The MapGraph API makes it easy to develop high performance graph analytics on GPUs. The API is based on the Gather-Apply-Scatter (GAS) model as used in GraphLab.  MapGraph is up to two orders of magnitude faster than parallel CPU implementations on up 24 CPU cores and has performance comparable to a state-of-the-art manually optimized GPU implementation. New algorithms can be implemented in a few hours that fully exploit the data-level parallelism of the GPU and offer throughput of up to 3.3 billion traversed edges per second on a single GPU. and up to 30 billion traversed edges per second on a cluster with 64 GPUs |
|Neo4j||2.1.3 (April 2014)||GPLv3 Community Edition. Commercial & AGPLv3 options for Enterprise and Advanced editions||Java||A highly scalable open source graph database that supports ACID, has high-availability clustering for enterprise deployments, and comes with a web-based administration tool that includes full transaction support and visual node-link graph explorer. Neo4j is accessible from most programming languages using its built-in REST web API interface. Neo4j is the most popular graph database in use today.|
|Orly|| (March 2014)||Apache 2||C++||A highly scalable open source graph database. Orly is accessible from most programming languages using its built-in REST web API interface. Orly is a popular graph database in use today.|
|OpenLink Virtuoso||7.1 (March 2014)||GPLv2 for Open Source Edition. Proprietary for Enterprise Edition.||C, C++||A hybrid database server handling RDF and other graph data, RDB/SQL data, XML data, filesystem documents/objects, and free text. May be deployed as a local embedded instance (as used in the Nepomuk Semantic Desktop), a single-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server.|
|Oracle Spatial and Graph||11.2 (2012)||Proprietary||Java, PL/SQL||1) RDF Semantic Graph: comprehensive W3C RDF graph management in Oracle Database with native reasoning and triple-level label security. 2) Network Data Model property graph: for physical/logical networks with persistent storage and a Java API for in-memory graph analytics.|
|Oracle NoSQL Database||2.0.39 (2013)||Proprietary||Java||RDF Graph for Oracle NoSQL Database is a feature of Enterprise Edition providing W3C RDF graph capabilities in NoSQL Database.|
|OrientDB||1.7.9 (September 2014)||Community Edition Apache 2, Enterprise Edition is Commercial||Java||OrientDB is a 2nd Generation Distributed Graph Database with the flexibility of Documents in one product with an Open Source commercial friendly license (Apache 2 license). It has a multi-master replication and sharding. Supports schema-less, schema-full and schema-mixed modes. Has a strong security profiling system based on user and roles and supports SQL amongst the query languages. Thanks to the SQL layer, it's straightforward to use for those skilled in the relational database world.|
|OQGRAPH||GPLv2||A graph computation engine for MySQL, MariaDB and Drizzle.|
|Ontotext OWLIM||5.3||OWLIM Lite is free
OWLIM SE and Enterprise are commercially licenced
|Java||A graph database engine, based entirely on Semantic Web standards from W3C: RDF, RDFS, OWL, SPARQL. OWLIM Lite is an "in memory" engine. OWLIM SE is robust standalone database engine. OWLIM Enterprise is a clustered version which offers horizontal scalability and failover support and other enterprise features.|
|R2DF||R2DF framework for ranked path queries over weighted RDF graphs.|
|ROIS||Freeware||Modula-2||A programmable knowledge server that supports inheritance and transitivity. Used in OpenGALEN as a Terminology Server.|
|sones GraphDB||AGPLv3||C#||A graph database and universal access layer (funded by Deutsche Telekom).|
|Sqrrl Enterprise||v1.5.1 (August 2014)||Proprietary||Java||Distributed, real-time graph database featuring cell-level security and massive scalability.|
|Stardog||v2.2 (July 2014)||Proprietary||Java||Fast, scalable, pure Java semantic graph database.|
|Teradata Aster||v6 (2013)||Proprietary||Java, SQL, Python, C++, R||A high performance, multi-purpose, highly scalable and extensible MPP database incorporating patented engines supporting native SQL, MapReduce and Graph data storage and manipulation. An extensive set of analytical function libraries and data visualization capabilities are also provided.|
|Titan||0.5.0 (2014)||Apache 2||Java||A distributed, real-time,scalable transactional graph database developed by Aurelius.|
|Trinity||C#, C, X64 Assembly||A distributed general purpose graph engine on a memory cloud.|
|VelocityGraph||Open source with proprietary back-end||C#||High performance, scalable & flexible graph database build with VelocityDB object database.|
|VertexDB||Revised BSD||C||A graph database server that supports automatic garbage collection.|
|WhiteDB||0.7.0 (October 2013)||GPLv3 and a free commercial licence||C||A graph/N-tuples shared memory database library.|
The following table compares the features of the above graph databases.
|Name||Graph Model||API||Query Methods||Visualizer||Consistency||Backend||Scalability|
|Bigdata||RDF||Java, Sesame, Blueprints, Gremlin, SPARQL, REST||SPARQL, Gremlin||Bigdata Workbench UI||MVCC/ACID||Native Java||Embedded, Client/Server, High Availability (HA)|
|Bitsy||Property Graph||Blueprints||Gremlin, Pixy||ACID with optimistic concurrency control||Human-readable JSON-encoded text files with checksums and markers for recovery|
|DEX/Sparksee||Labeled and directed attributed multigraph||Java, C++, .NET, Python||Native Java, C#, Python and C++ APIs, Blueprints, Gremlin||Exporting functionality to visualization formats||Consistency, durability and partial isolation and atomicity||Native graph. light and independent data structures with a small memory footprint for storage||Master/Slave replication|
|GraphBase Enterprise(1) GraphBase Agility(2)||(1) mixed, (2) Framework-managed Simple Graph||Java||Bounds Language, embedded java||GraphPad, BoundsPad, Navigator||ACID, graph-based transactions||proprietary native||(1) shared nothing distributed, (2) simple replication, 100+ Billion arcs per server|
|Horton||Attributed multigraph||Horton Query Language (Regular Language Expression + SQL)||C#, .Net Framework, Asynchronous communication protocols|
|HyperGraphDB||Object-oriented multi-relational labeled hypergraph||Custom,Java||MVCC/STM|
|IBM System G Native Store||Property Graph, RDF*||C++, Java, Python||Native Store gShell, Gremlin, SPARQL||Built-in Visualizer||ACID||Native Graph Storage||Both scale-up (using multithreading) and scale-out (using IBM PAMI)|
|InfiniteGraph||Labeled and directed multi-property graph||Java, Blueprints (Read Only)||Java (with parallel, distributed queries), Gremlin (Read Only)||Graph browser for developers. Plugins to allow use of external libraries.||ACID. There is also a parallel, loosely synchronized batch loader.||Objectivity/DB on standard filesystems||Distributed & Sharded. Objectivity/DB was the first DBMS to store a Petabyte of objects.|
|InfoGrid||Dynamically typed, object-oriented graph, multigraphs, semantic models|
|OpenLink Virtuoso||RDF graph: Triple & Quad (named graphs); expandable column store||SPARQL, XMLA, ODBC, JDBC, ADO.NET, OLE DB, Jena, Sesame, Virtuoso PL/SQL, Java, Python, Perl, PHP, HTTP, etc.||SPARQL 1.1; SPARQL web service endpoint; SQL; others||Pivot Viewer (Silverlight or HTML5); OpenLink Data Explorer; SPARQL-compliant tools; Apache Jena-based tools; XML & JSON-based tools; SQL based tools||ACID||Internal column-store or row-store (depending on licensure), hybrid RDF/SQL/RDB engine||Infinite via Commercial Edition's Cluster Module elastic cluster functionality; simple master-slave clustering of single-server instances also an option.|
|Oracle Spatial and Graph||RDF graph: Triple & Quad (named graphs); Network Data Model property graph||Java; Apache Jena; PL/SQL||SPARQL 1.1; SPARQL web service end point; SQL||SPARQL-compliant tools; Apache Jena-based tools; XML & JSON-based tools; SQL based tools||ACID||Efficient, compressed, partitioned graph storage; Native persisted in-database inferencing; SPARQL 1.1 & SQL integration; Triple-level label security; Semantic indexing of documents||Parallel load, query, inference; Query controls; Scales from PC to Oracle Exadata; Supports Oracle Real Application Clusters and Oracle Database 8 exabytes|
|Oracle NoSQL Database||RDF graph: Triple default graph, Triple & Quad named graphs||Java (Apache Jena)||SPARQL 1.1; SPARQL web service end point||SPARQL-compliant tools; Apache Jena-based tools; XML & JSON-based tools||ACID; Configurable consistency & durability policies||Key/value store; W3C SPARQL 1.1 & Update; In-memory RDFS/OWL inferencing||Parallel load/query; Query controls for: parallel execution, timeout, query optimization hints|
|Sqrrl Enterprise||Property Graph||Thrift, Blueprint||Own SQL-like query language and Java API||Integrates with 3rd party tools||Fully Consistent and ACID (transactions limited to a single graph node)||Accumulo||Distributed cluster with tens of trillions of edges|
|Stardog||RDF||Java, Sesame, Jena, SNARL, HTTP/REST, Python, Ruby, Node.js, C#, Clojure, Spring||SPARQL 1.1||Stardog Web, Pelorus||ACID||Native Graph Storage||50 billion RDF triples on $10,000 server|
|Titan||Property Graph||Java, Blueprints, REST, RexPro binary protocol, Python, Clojure (any language)||Gremlin, SPARQL||Integrates with 3rd party tools||ACID or Eventually Consistent||Cassandra, HBase, MapR M7 Tables, Berkeley DB, Persistit, Hazelcast||Distributed cluster (120 billion+ edges) or single server.|
|Trinity||Cell Based Graph Model||C#||Trinity Query Language||Cell level Atomicity||Native graph store and processing engine||billion node in-memory graph|