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DBMS > Hive vs. Kinetica vs. RDF4J vs. Sequoiadb vs. Splice Machine

System Properties Comparison Hive vs. Kinetica vs. RDF4J vs. Sequoiadb vs. Splice Machine

Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSequoiadb  Xexclude from comparisonSplice Machine  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopFully vectorized database across both GPUs and CPUsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.NewSQL database with distributed OLTP and SQLOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSRDF storeDocument store
Relational DBMS
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Score0.45
Rank#261  Overall
#41  Document stores
#122  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitehive.apache.orgwww.kinetica.comrdf4j.orgwww.sequoiadb.comsplicemachine.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.kinetica.comrdf4j.org/­documentationwww.sequoiadb.com/­en/­index.php?m=Files&a=indexsplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoinitially developed by FacebookKineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Sequoiadb Ltd.Splice Machine
Initial release20122012200420132014
Current release3.1.3, April 20227.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoServer: AGPL; Client: Apache V2Open 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 languageJavaC, C++JavaC++Java
Server operating systemsAll OS with a Java VMLinuxLinux
OS X
Unix
Windows
LinuxLinux
OS X
Solaris
Windows
Data schemeyesyesyes infoRDF Schemasschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes infooid, date, timestamp, binary, regexyes
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
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statementsnoSQL-like query languageyes
APIs and other access methodsJDBC
ODBC
Thrift
JDBC
ODBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
proprietary protocol using JSONJDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Java
PHP
Python
C++
Java
JavaScript (Node.js)
Python
Java
PHP
Python
.Net
C++
Java
PHP
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsyesJavaScriptyes infoJava
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationnoneSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infoIsolation support depends on the API usedDocument is locked during a transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoin-memory storage is supported as wellyesyes
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
User concepts infoAccess controlAccess rights for users, groups and rolesAccess rights for users and roles on table levelnosimple password-based access controlAccess rights for users, groups and roles according to SQL-standard

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More resources
HiveKineticaRDF4J infoformerly known as SesameSequoiadbSplice Machine
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