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DBMS > Fujitsu Enterprise Postgres vs. Realm vs. Solr vs. Splice Machine

System Properties Comparison Fujitsu Enterprise Postgres vs. Realm vs. Solr vs. Splice Machine

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Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonRealm  Xexclude from comparisonSolr  Xexclude from comparisonSplice Machine  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.A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataA widely used distributed, scalable search engine based on Apache LuceneOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSDocument storeSearch engineRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Score41.02
Rank#24  Overall
#3  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.postgresql.fastware.comrealm.iosolr.apache.orgsplicemachine.com
Technical documentationwww.postgresql.fastware.com/­product-manualsrealm.io/­docssolr.apache.org/­resources.htmlsplicemachine.com/­how-it-works
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyRealm, acquired by MongoDB in May 2019Apache Software FoundationSplice Machine
Initial release201420062014
Current releaseFujitsu Enterprise Postgres 14, January 20229.6.1, May 20243.1, March 2021
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoApache Version 2Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJavaJava
Server operating systemsLinux
Windows
Android
Backend: server-less
iOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Solaris
Windows
Data schemeyesyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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 indexesyesyesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLyesnoSolr Parallel SQL Interfaceyes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
RESTful HTTP/JSON API
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Java infowith Android only
Objective-C
React Native
Swift
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsno inforuns within the applications so server-side scripts are unnecessaryJava pluginsyes infoJava
Triggersyesyes infoChange Listenersyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashnoneShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoIn-Memory realmyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardyesyesAccess rights for users, groups and roles according to SQL-standard

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Fujitsu Enterprise PostgresRealmSolrSplice Machine
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