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DBMS > Kinetica vs. PostGIS vs. Spark SQL vs. Splice Machine

System Properties Comparison Kinetica vs. PostGIS vs. Spark SQL vs. Splice Machine

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonPostGIS  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  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 processingOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
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
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score21.72
Rank#29  Overall
#1  Spatial DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.kinetica.compostgis.netspark.apache.org/­sqlsplicemachine.com
Technical documentationdocs.kinetica.compostgis.net/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperKineticaApache Software FoundationSplice Machine
Initial release2012200520142014
Current release7.1, August 20213.4.2, February 20243.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoGPL v2.0Open Source infoApache 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++CScalaJava
Server operating systemsLinuxLinux
OS X
Windows
Linux
OS X
Solaris
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.noyesno
Secondary indexesyesyesnoyes
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
JDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsnoyes infoJava
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes infobased on PostgreSQLyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infobased on PostgreSQLnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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 RAMnonoyes
User concepts infoAccess controlAccess rights for users and roles on table levelyes infobased on PostgreSQLnoAccess rights for users, groups and roles according to SQL-standard

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More resources
KineticaPostGISSpark SQLSplice Machine
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