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DBMS > Kinetica vs. ObjectBox vs. RDFox vs. Spark SQL

System Properties Comparison Kinetica vs. ObjectBox vs. RDFox vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonObjectBox  Xexclude from comparisonRDFox  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsExtremely fast embedded database for small devices, IoT and MobileHigh performance knowledge graph and semantic reasoning engineSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSObject oriented DBMSGraph DBMS
RDF store
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.20
Rank#170  Overall
#5  Object oriented DBMS
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comobjectbox.iowww.oxfordsemantic.techspark.apache.org/­sql
Technical documentationdocs.kinetica.comdocs.objectbox.iodocs.oxfordsemantic.techspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaObjectBox LimitedOxford Semantic TechnologiesApache Software Foundation
Initial release2012201720172014
Current release7.1, August 20216.0, Septermber 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache License 2.0commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C and C++C++Scala
Server operating systemsLinuxAndroid
iOS
Linux
macOS
Windows
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeyesyesyes infoRDF schemasyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nonono
Secondary indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Proprietary native APIRESTful HTTP API
SPARQL 1.1
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C
Java
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnono
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationonline/offline synchronization between client and serverreplication via a shared file systemnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency in stand-alone mode, Eventual Consistency in replicated setups
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yes infoGPU vRAM or System RAMnoyesno
User concepts infoAccess controlAccess rights for users and roles on table levelyesRoles, resources, and access typesno
More information provided by the system vendor
KineticaObjectBoxRDFoxSpark SQL
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