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DBMS > Kinetica vs. PostGIS vs. Spark SQL vs. SQream DB

System Properties Comparison Kinetica vs. PostGIS vs. Spark SQL vs. SQream DB

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonPostGIS  Xexclude from comparisonSpark SQL  Xexclude from comparisonSQream DB  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsSpatial extension of PostgreSQLSpark SQL is a component on top of 'Spark Core' for structured data processinga GPU-based, columnar RDBMS for big data analytics workloads
Primary database modelRelational DBMSSpatial DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score22.69
Rank#29  Overall
#1  Spatial DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.70
Rank#227  Overall
#104  Relational DBMS
Websitewww.kinetica.compostgis.netspark.apache.org/­sqlsqream.com
Technical documentationdocs.kinetica.compostgis.net/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.sqream.com
DeveloperKineticaApache Software FoundationSQream Technologies
Initial release2012200520142017
Current release7.1, August 20213.4.2, February 20243.5.0 ( 2.13), September 20232022.1.6, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoGPL v2.0Open Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++CScalaC++, CUDA, Haskell, Java, Scala
Server operating systemsLinuxLinux
OS X
Windows
Linux
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes, ANSI Standard SQL Types
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 indexesyesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
.Net
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnouser defined functions in Python
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingyes infobased on PostgreSQLyes, utilizing Spark Corehorizontal and vertical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infobased on PostgreSQLnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
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.yes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users and roles on table levelyes infobased on PostgreSQLno

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More resources
KineticaPostGISSpark SQLSQream DB
DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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