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DBMS > Kinetica vs. Rockset vs. Splice Machine

System Properties Comparison Kinetica vs. Rockset vs. Splice Machine

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonRockset  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsA scalable, reliable search and analytics service in the cloud, built on RocksDBOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Relational DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.82
Rank#212  Overall
#36  Document stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.kinetica.comrockset.comsplicemachine.com
Technical documentationdocs.kinetica.comdocs.rockset.comsplicemachine.com/­how-it-works
DeveloperKineticaRocksetSplice Machine
Initial release201220192014
Current release7.1, August 20213.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++Java
Server operating systemsLinuxhostedLinux
OS X
Solaris
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesdynamic typingyes
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.nono infoingestion from XML files supported
Secondary indexesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsRead-only SQL queries, including JOINsyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP RESTJDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnoyes infoJava
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesMulti-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 configurationEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
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 RAMyes
User concepts infoAccess controlAccess rights for users and roles on table levelAccess rights for users and organizations can be defined via Rockset consoleAccess rights for users, groups and roles according to SQL-standard

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More resources
KineticaRocksetSplice Machine
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