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DBMS > Citus vs. Kinetica vs. NSDb vs. Splice Machine

System Properties Comparison Citus vs. Kinetica vs. NSDb vs. Splice Machine

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonKinetica  Xexclude from comparisonNSDb  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLFully vectorized database across both GPUs and CPUsScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSTime Series DBMSRelational DBMS
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.citusdata.comwww.kinetica.comnsdb.iosplicemachine.com
Technical documentationdocs.citusdata.comdocs.kinetica.comnsdb.io/­Architecturesplicemachine.com/­how-it-works
DeveloperKineticaSplice Machine
Initial release2010201220172014
Current release8.1, December 20187.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availablecommercialOpen Source infoApache Version 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

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC, C++Java, ScalaJava
Server operating systemsLinuxLinuxLinux
macOS
Linux
OS X
Solaris
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, stringyes
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.yes infospecific XML type available, but no XML query functionalitynono
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyes infostandard, with numerous extensionsSQL-like DML and DDL statementsSQL-like query languageyes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
gRPC
HTTP REST
WebSocket
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
Java
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functionsnoyes infoJava
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users and roles on table levelAccess rights for users, groups and roles according to SQL-standard

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More resources
CitusKineticaNSDbSplice Machine
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