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DBMS > Kinetica vs. Kingbase vs. Spark SQL vs. Ultipa

System Properties Comparison Kinetica vs. Kingbase vs. Spark SQL vs. Ultipa

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonKingbase  Xexclude from comparisonSpark SQL  Xexclude from comparisonUltipa  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsAn enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.Spark SQL is a component on top of 'Spark Core' for structured data processingHigh performance Graph DBMS supporting HTAP high availability cluster deployment
Primary database modelRelational DBMSRelational DBMSRelational DBMSGraph DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.50
Rank#257  Overall
#119  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.19
Rank#330  Overall
#30  Graph DBMS
Websitewww.kinetica.comwww.kingbase.com.cnspark.apache.org/­sqlwww.ultipa.com
Technical documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.ultipa.com/­document
DeveloperKineticaBeiJing KINGBASE Information technologies inc.Apache Software FoundationUltipa
Initial release2012199920142019
Current release7.1, August 2021V8.0, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial
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 languageC, C++C and JavaScala
Server operating systemsLinuxLinux
Windows
Linux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.noyesno
Secondary indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsStandard with numerous extensionsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Java
Python
R
Scala
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsno
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by range, list and hash) and vertical partitioningyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesnone
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 Consistency
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users and roles on table levelfine grained access rights according to SQL-standardno

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More resources
KineticaKingbaseSpark SQLUltipa
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