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

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

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonKinetica  Xexclude from comparisonObjectBox  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityFully 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 Spark
Primary database modelRelational DBMSRelational DBMSObject oriented DBMSSearch 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
Score1.59
Rank#150  Overall
#69  Relational DBMS
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.datomic.comwww.kinetica.comobjectbox.iosphinxsearch.comsplicemachine.com
Technical documentationdocs.datomic.comdocs.kinetica.comdocs.objectbox.iosphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperCognitectKineticaObjectBox LimitedSphinx Technologies Inc.Splice Machine
Initial release20122012201720012014
Current release1.0.6735, June 20237.1, August 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoApache License 2.0Open 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 languageJava, ClojureC, C++C and C++C++Java
Server operating systemsAll OS with a Java VMLinuxAndroid
iOS
Linux
macOS
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 SQLnoSQL-like DML and DDL statementsnoSQL-like query language (SphinxQL)yes
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
RESTful HTTP API
Proprietary native APIProprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
Java
C++
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
Server-side scripts infoStored proceduresyes infoTransaction Functionsuser defined functionsnonoyes infoJava
TriggersBy using transaction functionsyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingnoneSharding 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 nodesnone infoBut extensive use of caching in the application peersSource-replica replicationonline/offline synchronization between client and servernoneMulti-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 ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes 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 inforecommended only for testing and developmentyes infoGPU vRAM or System RAMnoyes
User concepts infoAccess controlnoAccess rights for users and roles on table levelyesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatomicKineticaObjectBoxSphinxSplice Machine
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