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DBMS > HugeGraph vs. Kinetica vs. NSDb vs. Stardog

System Properties Comparison HugeGraph vs. Kinetica vs. NSDb vs. Stardog

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Editorial information provided by DB-Engines
NameHugeGraph  Xexclude from comparisonKinetica  Xexclude from comparisonNSDb  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionA fast-speed and highly-scalable Graph DBMSFully vectorized database across both GPUs and CPUsScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelGraph DBMSRelational DBMSTime Series DBMSGraph DBMS
RDF store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websitegithub.com/­hugegraph
hugegraph.apache.org
www.kinetica.comnsdb.iowww.stardog.com
Technical documentationhugegraph.apache.org/­docsdocs.kinetica.comnsdb.io/­Architecturedocs.stardog.com
DeveloperBaiduKineticaStardog-Union
Initial release2018201220172010
Current release0.97.1, August 20217.3.0, May 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0commercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
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 languageJavaC, C++Java, ScalaJava
Server operating systemsLinux
macOS
Unix
LinuxLinux
macOS
Linux
macOS
Windows
Data schemeyesyesschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, stringyes
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.nononono infoImport/export of XML data possible
Secondary indexesyes infoalso supports composite index and range indexyesall fields are automatically indexedyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query languageYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsJava API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP API
gRPC
HTTP REST
WebSocket
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesGroovy
Java
Python
C++
Java
JavaScript (Node.js)
Python
Java
Scala
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresasynchronous Gremlin script jobsuser defined functionsnouser defined functions and aggregates, HTTP Server extensions in Java
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseSource-replica replicationMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityyes infoedges in graphyesnoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlUsers, roles and permissionsAccess rights for users and roles on table levelAccess rights for users and roles

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More resources
HugeGraphKineticaNSDbStardog
Recent citations in the news

POC exploit code published for 9.8-rated Apache HugeGraph RCE flaw
7 June 2024, The Register

Critical Apache HugeGraph Flaw Let Attackers Execute Remote Code
23 April 2024, GBHackers

PoC Exploit Released for High Severity Apache HugeGraph RCE flaw
7 June 2024, CybersecurityNews

AI, Lockbit, Veeam, Club Penguin, Kali, Commando Cat, HugeGraph, Aaran Leyland… – SWN #391
7 June 2024, SC Media

Top 5 CVEs and Vulnerabilities of May 2024
3 June 2024, Security Boulevard

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21 March 2024, insideBIGDATA

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26 March 2024, Datanami

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica Launches Generative AI Solution for Real-Time Inferencing Powered by NVIDIA AI Enterprise
18 March 2024, GlobeNewswire

Transforming spatiotemporal data analysis with GPUs and generative AI
30 October 2023, InfoWorld

provided by Google News



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