Graph Database Management (GDBMS) Platform

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A Graph Database Management (GDBMS) Platform is a DBMS platform that can perform graph data operations for graph datasets.

References

2018a

  • (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Graph_database Retrieved:2018-6-18.
    • In computing, a graph database (GDB ) is a database that uses graph structures for semantic queries with nodes, edges and properties to represent and store data. A key concept of the system is the graph (or edge or relationship), which directly relates data items in the store. The relationships allow data in the store to be linked together directly, and in many cases retrieved with one operation. This contrasts with relational databases that, with the aid of relational database management systems, permit managing the data without imposing implementation aspects like physical record chains; for example, links between data are stored in the database itself at the logical level, and relational algebra operations (e.g. join) can be used to manipulate and return related data in the relevant logical format. The execution of relational queries is possible with the aid of the database management systems at the physical level (e.g. using indexes), which permits boosting performance without modifying the logical structure of the database. Graph databases, by design, allow simple and fast retrievalof complex hierarchical structures that are difficult to modelin relational systems. 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 and lack easy traversal over a chain of edges.

      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. Mostgraph databases based on non-relational storage engines also add the concept of tags or properties, which are essentially relationships having a pointer to another document. This allows data elements to be categorized for easy retrieval en masse.

      Retrieving data from a graph database requires a query language other than SQL, which was designed for the manipulation of data in a relational system and therefore cannot “elegantly” handle traversing a graph. , no single graph query language has been universally adopted in the same way as SQL was for relational databases, and there are a wide variety of systems, most often tightly tied to one product. Some standardization efforts have occurred, leading to multi-vendor query languages like Gremlin, SPARQL, and Cypher. In addition to having query language interfaces, some graph databases are accessed through application programming interfaces (APIs).

2018b

Name Version License Language Description
GraphDB 8.4 (January 2018) Template:Some Java, .NET, C#, Clojure, JavaScript, PHP, Python, Ruby GraphDB is a high-performance semantic repository created by Ontotext. It is implemented in Java and packaged as a Storage and Inference Layer (SAIL) for the RDF4J. Loading, reasoning and query evaluation proceed fast even against huge ontologies and knowledge bases. Compliance to W3C standards, performant, extensible, scalable, high-availability cluster, expressive, rich and flexible data model. GraphDB is most commonly used in scenarios with high model complexity, where semantic context and data quality are important. GraphDB data are used as a standard for master or reference data in the organization.[1]
AgensGraph[2] 1.3.1 (March 2018) Free, Apache 2 C Hybrid-database integrated with Relational database and Graph database. SQL and Cypher can be used in the same query in AgensGraph.
AllegroGraph 5.1 (May 2015) Template:Some C#, C, Common Lisp, Java, Python Resource Description Framework (RDF) and graph database
AnzoGraph[3] 4.0 (February 2018) Template:Proprietary C, C++ AnzoGraph is a Massively parallel native graph GOLAP (Graph Online Analytics Processing) style database built to support complex SPARQL join queries and analyze trillions of relationships. AnzoGraph is designed for interactive analysis of broad swaths of RDF data, accumulated over weeks or years of transactions, possibly from many disparate GOLTP and other database sources.[4][5][6]
ArangoDB 3.2.0 (July 2017) Template:Some, C++, JavaScript The most popular (as of 2015) NoSQL database available under an open source license and that provides both document store and triple store abilities[7]
Blazegraph 2.1 (April 2016) Template:Some Java RDF-graph database capable of clustered deployment and graphics processing unit (GPU), in commercial version; supports high availability (HA) mode, embedded mode, single server mode. Supports the Blueprints and SPARQL.[8][9]
Cayley 0.7.3 (April 2018) Template:Free, Apache 2 Go Graph database[10]
Dgraph 1.0.4 (March 2018) Template:Free, Apache 2 Go Open source, scalable, distributed, highly available, transactional and fast graph database, designed from ground up to be run at web scale.[11][12]
DataStax Enterprise Graph v6.0.1 (June 2018) Template:Proprietary Java Distributed, real-time, scalable database inspired by Titan; supports Tinkerpop and integrates with Cassandra[13]
Sparksee[14] 5.2.0 (2015) Template: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 & exploring large networks; has bindings for Java, C++, C#, Python, and Objective-C; version 5 is the first graph mobile database
GraphBase[15] 1.0.03b Template:Proprietary, commercial Java A customizable, distributed, small size graph store with a rich tool set from FactNexus.
gStore[16] 0.4.1 (March 2017) Template:Some C++ An engine to manage large graph-structured data; open-source for Linux operating systems; written fully in C++, with some libraries such as readline, antlr, etc.; use modes: native, server-client, or distributed.[17]
InfiniteGraph 3.0 (January 2013) Template:Proprietary, commercial Java Distributed and cloud-enabled
JanusGraph 0.2.0 (October 2017) Template:Free, Apache 2 Java Distributed graph database forked from Titan[18][19][20][21]
MarkLogic 8.0.4 (2015) Template: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 and a full-list of enterprise features such as ACID transactions, high availability and disaster recovery, certified security, scalability, and elasticity
Neo4j 3.3.5 (April 2018)[22] Template:Some Java, .NET, JavaScript, Python, Ruby Highly scalable open source, 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; accessible from most programming languages using its built-in REST web API interface, and a proprietary Bolt protocol with official drivers; most popular graph database in use as of January 2017[23]
OpenLink Virtuoso 8.0 (September 2017) Template:Some C, C++ 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 one-instance network server, or a shared-nothing elastic-cluster multiple-instance networked server[24]
Oracle Spatial and Graph; part of Oracle Database 12.1.0.2 (2014) Template: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
OrientDB 2.2.24 (July 2017) Template:Some Java Second generation distributed graph database with the flexibility of documents in one product (i.e., it is both a graph database and a document NoSQL database at the same time); it has an open source commercial friendly (Apache 2) license; and is a highly scalable with full ACID support; it has a multi-master replication and sharding; supports schema-less, -full, and -mixed modes; has a strong security profiling system based on user and roles; supports a query language that is so similar to SQL which is friendly to those coming from a SQL and relational database background decreasing the learning curve needed. It has HTTP REST + JSON API.
SAP HANA SPS12 Revision 120 Template:Proprietary C, C++, Java, JavaScript & SQL-like language In-memory ACID transaction supported property graph[25]
Sqrrl Enterprise 2.0 (February 2015) Template:Proprietary Java Distributed, real-time graph database featuring cell-level security and mass-scalability[26]
Teradata Aster 7 (2016) Template:Proprietary Java, SQL, Python, C++, R High performance, multi-purpose, highly scalable, and extensible MPP database incorporating patented engines supporting native SQL, MapReduce and Graph data storage and manipulation; provides an extensive set of analytic function libraries and data visualization abilities[27]
TigerGraph[28] 2.0 (2018) Template:Some C++ Serve collaboration and security with Multi-Graph, high performance, high scalability, native MPP real-time graph database, supporting high expressive GSQL, RESTful API, and visualization GraphStudio SDK.
Microsoft SQL Server 2017[29] RC1 Template: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 receive the benefits of using SQL Server as the foundational database management system.
FlureeDB V1 Beta Free, Proprietary GraphQL, FlureeQL Graph Database that enables Blockchain-backed applications. ACID compliant; built for distributed ledger applications.

2016

2015

  1. ↑ http://www.zdnet.com/article/graph-databases-and-rdf-its-a-family-affair/
  2. ↑ "Graph DBMS Performance Comparison AgensGraph vs. Neo4j" (in en). https://www.businesswire.com/news/home/20170629005344/en/Graph-DBMS-Performance-Comparison-AgensGraph-vs.-Neo4j. Retrieved 2018-01-15. 
  3. ↑ "In-Memory Massively Parallel Distributed Graph Database Purpose-built for Analytics" (in en). https://www.cambridgesemantics.com/product/anzograph/. Retrieved 2018-02-20. 
  4. ↑ Rueter, John (February 15, 2018). "Cambridge Semantics Announces AnzoGraph Graph-Based Analytics Support for Amazon Neptune and Graph Databases". https://www.businesswire.com/news/home/20180215006023/en. Retrieved February 20, 2018. 
  5. ↑ Zane, Barry (November 2, 2016). "Semantic Graph Databases: A worthy successor to relational databases". http://www.dbta.com/BigDataQuarterly/Articles/Semantic-Graph-Databases-A-worthy-successor-to-relational-databases-114569.aspx. Retrieved February 20, 2018. 
  6. ↑ Template:Cite news
  7. ↑ Fowler, Adam (February 24, 2015). NoSQL for Dummies. John Wiley & Sons. pp. 298–. ISBN 978-1-118-90574-6. https://books.google.com/books?id=g_QwBgAAQBAJ&pg=PA298. 
  8. ↑ Vaughan, Jack (January 25, 2016). "Beyond gaming, GPU technology takes on graphs, machine learning". http://searchdatamanagement.techtarget.com/news/4500271645/Beyond-gaming-GPU-technology-takes-on-graphs-machine-learning. Retrieved May 9, 2017. 
  9. ↑ Yegulalp, Serdar (September 26, 2016). "Faster with GPUs: 5 turbocharged databases". http://www.infoworld.com/article/3123747/data-center/faster-with-gpus-5-turbocharged-databases.html. Retrieved May 9, 2017. 
  10. ↑ "Google Releases Cayley Open-Source Graph Database". November 13, 2014. http://www.eweek.com/database/google-releases-cayley-open-source-graph-database.html. Retrieved May 9, 2017. 
  11. ↑ "Ex-Googler startup DGraph Labs raises US$1.1 million in seed funding round to build industry’s first open source, native and distributed graph database". May 17, 2016. https://globenewswire.com/news-release/2016/05/17/840895/0/en/Ex-Googler-startup-DGraph-Labs-raises-US-1-1-million-in-seed-funding-round-to-build-industry-s-first-open-source-native-and-distributed-graph-database.html. Retrieved July 31, 2017. 
  12. ↑ Bailey, Michael (May 18, 2016). "Cannon-Brookes, Blackbird, Bain back new migrant's graph start-up". The Australian Financial Review. http://www.afr.com/technology/cannonbrookes-blackbird-bain-back-new-migrants-graph-startup-20160517-goxglw. Retrieved July 31, 2017. 
  13. ↑ Woodie, Alex (June 21, 2016). "Beyond Titan: The Evolution of DataStax’s New Graph Database". https://www.datanami.com/2016/06/21/beyond-titan-evolution-datastaxs-new-graph-database/. Retrieved May 9, 2017. 
  14. ↑ "Sparksee high-performance graph database". http://sparsity-technologies.com#sparksee. Retrieved May 9, 2017. 
  15. ↑ Longbottom, Clive (May 1, 2016). "Graph databases: What are the benefits for CIOs?". http://www.computerweekly.com/opinion/Graph-databases-What-are-the-benefits-for-CIOs. Retrieved May 9, 2017. 
  16. ↑ "gStore Graph Database Engine". http://www.gstore-pku.com/en/. 
  17. ↑ Zou, Lei; Özsu, M. Tamer; Chen, Lei; Shen, Xuchuan; Huang, Ruizhe; Zhao, Dongyan (August 2014). "gStore: a graph-based SPARQL query engine". https://link.springer.com/article/10.1007/s00778-013-0337-7. 
  18. ↑ "The Linux Foundation Welcomes JanusGraph". January 12, 2017. https://www.linuxfoundation.org/blog/the-linux-foundation-welcomes-janusgraph/. Retrieved February 10, 2018. 
  19. ↑ Brukman, Misha (January 12, 2017). "JanusGraph connects the past and future of Titan". https://opensource.googleblog.com/2017/01/janusgraph-connects-past-and-future-of-titan.html. Retrieved February 10, 2018. 
  20. ↑ He, Jing Chen (January 16, 2017). "JanusGraph – A Graph DB that carries forward the legacy of Titan". https://developer.ibm.com/hadoop/2017/01/16/janusgraph-graph-db-carries-forward-legacy-titan/. Retrieved May 9, 2017. 
  21. ↑ Woodie, Alex (January 13, 2017). "JanusGraph Picks Up Where TitanDB Left Off". https://www.datanami.com/2017/01/13/janusgraph-picks-titandb-left-off/. Retrieved May 9, 2017. 
  22. ↑ "Release Notes: Neo4j 3.1.1". https://neo4j.com/release-notes/neo4j-3-1-1/. Retrieved May 9, 2017. 
  23. ↑ "Ranking of Graph DBMS". http://db-engines.com/en/ranking/graph+dbms. Retrieved May 9, 2017. 
  24. ↑ "Clustering Deployment Architecture Diagrams for Virtuoso". Virtuoso Open-Source Wiki. OpenLink Software. http://virtuoso.openlinksw.com/dataspace/dav/wiki/Main/VirtClusteringDiagrams. Retrieved May 9, 2017. 
  25. ↑ Template:Cite conference
  26. ↑ Vanian, Jonathan (18 February 2015). "NSA-linked Sqrrl eyes cyber security and lands $7M in funding". https://gigaom.com/2015/02/18/nsa-linked-sqrrl-eyes-cyber-security-and-lands-7m-in-funding/. Retrieved May 9, 2017. 
  27. ↑ Woodie, Alex (October 23, 2015). "The Art of Analytics, Or What the Green-Haired People Can Teach Us". https://www.datanami.com/2015/10/23/the-art-of-analytics-or-what-the-green-haired-people-can-teach-us/. Retrieved May 9, 2017. 
  28. ↑ "TigerGraph 2.0 Helps Enterprises Roar with the Fastest and Most Scalable Graph Analytics" (in en-US). 2018-02-27. https://globenewswire.com/news-release/2018/02/27/1396073/0/en/TigerGraph-2-0-Helps-Enterprises-Roar-with-the-Fastest-and-Most-Scalable-Graph-Analytics.html. Retrieved 2018-02-27. 
  29. ↑ "What's New in SQL Server 2017". April 19, 2017. https://docs.microsoft.com/en-us/sql/sql-server/what-s-new-in-sql-server-2017. Retrieved May 9, 2017. 
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