DBMS > DataFS vs. Faircom DB vs. OushuDB vs. RDF4J

System Properties Comparison DataFS vs. Faircom DB vs. OushuDB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Relational DBMS

RDF store

Secondary database models

Graph DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	0.05
Rank	#357	Overall
	#151	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

newdatabase.com

www.faircom.com/products/faircom-db

www.oushu.com/product/oushuDB

rdf4j.org

Technical documentation

dev.mobiland.com/Overview.xsp

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.oushu.com/documentation

rdf4j.org/documentation

Developer

Mobiland AG

FairCom Corporation

Oushu

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2018

1979

2004

Current release

1.1.263, October 2022

V13, July 2024

4.0.1, August 2020

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

ANSI C, C++

Java

Server operating systems

Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Linux
OS X
Unix
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema free, schema optional, schema required, partial schema,

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

Full-featured ANSI SQL support

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C
C#
C++
VB.Net

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C
C++

Java
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

tunable from ACID to Eventually Consistent

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Windows-Profile

Fine grained access rights according to SQL-standard with additional protections for files

Kerberos, SSL and role based access

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