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DBMS > Fujitsu Enterprise Postgres vs. Kinetica vs. RDF4J vs. Solr

System Properties Comparison Fujitsu Enterprise Postgres vs. Kinetica vs. RDF4J vs. Solr

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Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Fully vectorized database across both GPUs and CPUsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSRDF storeSearch engine
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
Time Series DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitewww.postgresql.fastware.comwww.kinetica.comrdf4j.orgsolr.apache.org
Technical documentationwww.postgresql.fastware.com/­product-manualsdocs.kinetica.comrdf4j.org/­documentationsolr.apache.org/­resources.html
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyKineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release201220042006
Current releaseFujitsu Enterprise Postgres 14, January 20227.1, August 20219.6.1, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC, C++JavaJava
Server operating systemsLinux
Windows
LinuxLinux
OS X
Unix
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyes infoRDF Schemasyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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.noyes
Secondary indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesSQL-like DML and DDL statementsnoSolr Parallel SQL Interface
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Java API
RESTful HTTP/JSON API
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
Java
PHP
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functionsuser defined functionsyesJava plugins
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Foreign keys infoReferential integrityyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoIsolation support depends on the API usedoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyes infoin-memory storage is supported as wellyes
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 controlfine grained access rights according to SQL-standardAccess rights for users and roles on table levelnoyes

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More resources
Fujitsu Enterprise PostgresKineticaRDF4J infoformerly known as SesameSolr
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