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DBMS > Kinetica vs. ObjectBox vs. Realm vs. Sphinx vs. Splice Machine

System Properties Comparison Kinetica vs. ObjectBox vs. Realm vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonObjectBox  Xexclude from comparisonRealm  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsExtremely fast embedded database for small devices, IoT and MobileA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataOpen 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 Spark
Primary database modelRelational DBMSObject oriented DBMSDocument storeSearch engineRelational 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
Score7.60
Rank#52  Overall
#9  Document stores
Score5.98
Rank#56  Overall
#5  Search engines
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.kinetica.comobjectbox.iorealm.iosphinxsearch.comsplicemachine.com
Technical documentationdocs.kinetica.comdocs.objectbox.iorealm.io/­docssphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperKineticaObjectBox LimitedRealm, acquired by MongoDB in May 2019Sphinx Technologies Inc.Splice Machine
Initial release20122017201420012014
Current release7.1, August 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache License 2.0Open SourceOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
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
Android
Backend: server-less
iOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.nonono
Secondary indexesyesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoSQL-like query language (SphinxQL)yes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Proprietary native APIProprietary protocolJDBC
Native Spark Datasource
ODBC
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
.Net
Java infowith Android only
Objective-C
React Native
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
Server-side scripts infoStored proceduresuser defined functionsnono inforuns within the applications so server-side scripts are unnecessarynoyes infoJava
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes infoChange Listenersnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnonenoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationonline/offline synchronization between client and servernonenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnoyes infoIn-Memory realmyes
User concepts infoAccess controlAccess rights for users and roles on table levelyesyesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
KineticaObjectBoxRealmSphinxSplice Machine
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