DBMS > Faircom DB vs. mSQL vs. Oracle Coherence vs. RDF4J vs. ToroDB

System Properties Comparison Faircom DB vs. mSQL vs. Oracle Coherence vs. RDF4J vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Oracles in-memory data grid solution

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store
Relational DBMS

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	1.97
Rank	#129	Overall
	#22	Key-value stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.faircom.com/products/faircom-db

hughestech.com.au/products/msql

www.oracle.com/java/coherence

rdf4j.org

github.com/torodb/server

Technical documentation

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

docs.oracle.com/en/middleware/standalone/coherence

rdf4j.org/documentation

Developer

FairCom Corporation

Hughes Technologies

Oracle

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

8Kdata

Initial release

1979

1994

2007

2004

2016

Current release

V12, November 2020

4.4, October 2021

14.1, August 2023

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

free licenses can be provided

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

Java

Server operating systems

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

easily portable to other OSs

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java VM

Linux
OS X
Unix
Windows

All OS with a Java 7 VM

Data scheme

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

yes

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

string, integer, double, boolean, date, object_id

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

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

JDBC
ODBC

JCache
JPA
RESTful HTTP API

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C++
Java

Java
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

none

Sharding

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

yes, with selectable consistency level

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

none

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

configurable

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes

optionally

yes

in-memory storage is supported as well

yes

In-memory capabilities

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

yes

User concepts

Access control

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

authentification to access the cache via certificates or http basic authentication

Access rights for users and roles

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