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DBMS > GeoSpock vs. Kinetica vs. Linter vs. Spark SQL

System Properties Comparison GeoSpock vs. Kinetica vs. Linter vs. Spark SQL

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Editorial information provided by DB-Engines
NameGeoSpock  Xexclude from comparisonKinetica  Xexclude from comparisonLinter  Xexclude from comparisonSpark SQL  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionSpatial and temporal data processing engine for extreme data scaleFully vectorized database across both GPUs and CPUsRDBMS for high security requirementsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsTime Series DBMSSpatial DBMS
Time Series DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegeospock.comwww.kinetica.comlinter.ruspark.apache.org/­sql
Technical documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGeoSpockKineticarelex.ruApache Software Foundation
Initial release201219902014
Current release2.0, September 20197.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, JavascriptC, C++C and C++Scala
Server operating systemshostedLinuxAIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Linux
OS X
Windows
Data schemeyesyesyesyes
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.nononono
Secondary indexestemporal, categoricalyesyesno
SQL infoSupport of SQLANSI SQL for query only (using Presto)SQL-like DML and DDL statementsyesSQL-like DML and DDL statements
APIs and other access methodsJDBCJDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functionsyes infoproprietary syntax with the possibility to convert from PL/SQLno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
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.noyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users can be defined per tableAccess rights for users and roles on table levelfine grained access rights according to SQL-standardno

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More resources
GeoSpockKineticaLinterSpark SQL
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