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DBMS > Kinetica vs. ObjectBox vs. Sphinx vs. Splice Machine vs. Teradata Aster

System Properties Comparison Kinetica vs. ObjectBox vs. Sphinx vs. Splice Machine vs. Teradata Aster

Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonObjectBox  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionFully vectorized database across both GPUs and CPUsExtremely fast embedded database for small devices, IoT and MobileOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkPlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSObject oriented DBMSSearch engineRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.20
Rank#170  Overall
#5  Object oriented DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.kinetica.comobjectbox.iosphinxsearch.comsplicemachine.com
Technical documentationdocs.kinetica.comdocs.objectbox.iosphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperKineticaObjectBox LimitedSphinx Technologies Inc.Splice MachineTeradata
Initial release20122017200120142005
Current release7.1, August 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache License 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C and C++C++Java
Server operating systemsLinuxAndroid
iOS
Linux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
Data schemeyesyesyesyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesnoyesyes
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.nonoyes infoin Aster File Store
Secondary indexesyesyesyes infofull-text index on all search fieldsyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like query language (SphinxQL)yesyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Proprietary native APIProprietary protocolJDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresuser defined functionsnonoyes infoJavaR packages
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar PartitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationonline/offline synchronization between client and servernoneMulti-source replication
Source-replica replication
yes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integrationyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnoyesno
User concepts infoAccess controlAccess rights for users and roles on table levelyesnoAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
KineticaObjectBoxSphinxSplice MachineTeradata Aster
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