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DBMS > AgensGraph vs. AllegroGraph vs. Kinetica vs. RavenDB

System Properties Comparison AgensGraph vs. AllegroGraph vs. Kinetica vs. RavenDB

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonAllegroGraph  Xexclude from comparisonKinetica  Xexclude from comparisonRavenDB  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLHigh performance, persistent RDF store with additional support for Graph DBMSFully vectorized database across both GPUs and CPUsOpen Source Operational and Transactional Enterprise NoSQL Document Database
Primary database modelGraph DBMS
Relational DBMS
Document store infowith version 6.5
Graph DBMS
RDF store
Vector DBMS
Relational DBMSDocument store
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
Graph DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#320  Overall
#29  Graph DBMS
#143  Relational DBMS
Score0.84
Rank#204  Overall
#35  Document stores
#18  Graph DBMS
#7  RDF stores
#10  Vector DBMS
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score2.68
Rank#102  Overall
#19  Document stores
Websitebitnine.net/­agensgraphallegrograph.comwww.kinetica.comravendb.net
Technical documentationbitnine.net/­documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmldocs.kinetica.comravendb.net/­docs
DeveloperBitnine Global Inc.Franz Inc.KineticaHibernating Rhinos
Initial release2016200420122010
Current release2.1, December 20188.0, December 20237.1, August 20215.4, July 2022
License infoCommercial or Open SourceOpen Source infoApache License 2.0commercial infoLimited community edition freecommercialOpen Source infoAGPL version 3, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC, C++C#
Server operating systemsLinux
OS X
Windows
Linux
OS X
Windows
LinuxLinux
macOS
Raspberry Pi
Windows
Data schemedepending on used data modelyes infoRDF schemasyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono infobulk load of XML files possibleno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesSPARQL is used as query languageSQL-like DML and DDL statementsSQL-like query language (RQL)
APIs and other access methodsCypher Query Language
JDBC
RESTful HTTP API
SPARQL
JDBC
ODBC
RESTful HTTP API
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
Supported programming languagesC
Java
JavaScript
Python
C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesyes infoJavaScript or Common Lispuser defined functionsyes
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritancewith FederationShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
Source-replica replicationMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency or Eventual Consistency depending on configurationDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID, Cluster-wide transaction available
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoGPU vRAM or System RAM
User concepts infoAccess controlfine grained access rights according to SQL-standardUsers with fine-grained authorization concept, user roles and pluggable authenticationAccess rights for users and roles on table levelAuthorization levels configured per client per database
More information provided by the system vendor
AgensGraphAllegroGraphKineticaRavenDB
News

AllegroGraph Named a 2024 Trend Setting Product
16 September 2024

Gartner recognized Franz Inc. as a Key Neuro-Symbolic AI Provider in 2024 Hype Cycle for AI
10 September 2024

Next-Generation Cybersecurity: Integrating Knowledge Graphs and Neuro-Symbolic AI with STIX and TAXII
10 September 2024

Pruning Cycles in UMLS Metathesaurus: A Neuro Symbolic AI Approach
9 September 2024

Awesome AI Companies – Franz Inc.
17 July 2024

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More resources
AgensGraphAllegroGraphKineticaRavenDB
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