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

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

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Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonKinetica  Xexclude from comparisonRDFox  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchFully vectorized database across both GPUs and CPUsHigh performance knowledge graph and semantic reasoning engineSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelTime Series DBMSRelational DBMSGraph DBMS
RDF store
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­spotify/­heroicwww.kinetica.comwww.oxfordsemantic.techspark.apache.org/­sql
Technical documentationspotify.github.io/­heroicdocs.kinetica.comdocs.oxfordsemantic.techspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperSpotifyKineticaOxford Semantic TechnologiesApache Software Foundation
Initial release2014201220172014
Current release7.1, August 20216.0, Septermber 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen 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 languageJavaC, C++C++Scala
Server operating systemsLinuxLinux
macOS
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyes 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 indexesyes infovia Elasticsearchyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statementsnoSQL-like DML and DDL statements
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
SPARQL 1.1
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
Java
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationreplication via a shared file systemnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency in stand-alone mode, Eventual Consistency in replicated setups
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
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.noyes infoGPU vRAM or System RAMyesno
User concepts infoAccess controlAccess rights for users and roles on table levelRoles, resources, and access typesno

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HeroicKineticaRDFoxSpark SQL
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