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DBMS > Fujitsu Enterprise Postgres vs. Lovefield vs. Microsoft Azure Table Storage vs. ObjectBox vs. Solr

System Properties Comparison Fujitsu Enterprise Postgres vs. Lovefield vs. Microsoft Azure Table Storage vs. ObjectBox vs. Solr

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonLovefield  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonObjectBox  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.Embeddable relational database for web apps written in pure JavaScriptA Wide Column Store for rapid development using massive semi-structured datasetsExtremely fast embedded database for small devices, IoT and MobileA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSWide column storeObject oriented DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Time Series DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score0.33
Rank#286  Overall
#131  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score1.29
Rank#166  Overall
#5  Object oriented DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitewww.postgresql.fastware.comgoogle.github.io/­lovefieldazure.microsoft.com/­en-us/­services/­storage/­tablesobjectbox.iosolr.apache.org
Technical documentationwww.postgresql.fastware.com/­product-manualsgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mddocs.objectbox.iosolr.apache.org/­resources.html
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyGoogleMicrosoftObjectBox LimitedApache Software Foundation
Initial release2014201220172006
Current releaseFujitsu Enterprise Postgres 14, January 20222.1.12, February 20179.6.1, May 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen Source infoApache License 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJavaScriptC and C++Java
Server operating systemsLinux
Windows
server-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafarihostedAndroid
iOS
Linux
macOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyesyes 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.nononoyes
Secondary indexesyesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesSQL-like query language infovia JavaScript builder patternnonoSolr Parallel SQL Interface
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
RESTful HTTP APIProprietary native APIJava API
RESTful HTTP/JSON API
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
JavaScript.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
.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 functionsnononoJava plugins
TriggersyesUsing read-only observersnonoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashnoneSharding infoImplicit feature of the cloud servicenoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.online/offline synchronization between client and serveryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDoptimistic lockingACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
Durability infoSupport for making data persistentyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infousing MemoryDBnonoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardnoAccess rights based on private key authentication or shared access signaturesyesyes
More information provided by the system vendor
Fujitsu Enterprise PostgresLovefieldMicrosoft Azure Table StorageObjectBoxSolr
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more
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