DBMS > Faircom EDGE vs. mSQL vs. RDF4J vs. Solr vs. ToroDB

System Properties Comparison Faircom EDGE vs. mSQL vs. RDF4J vs. Solr vs. ToroDB

Editorial information provided by DB-Engines

Name

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

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

mSQL (Mini SQL) is a simple and lightweight RDBMS

RDF4J 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

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

Primary database model

Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.faircom.com/products/faircom-edge

hughestech.com.au/products/msql

rdf4j.org

solr.apache.org

github.com/torodb/server

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

rdf4j.org/documentation

solr.apache.org/resources.html

Developer

FairCom Corporation

Hughes Technologies

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

Apache Software Foundation

8Kdata

Initial release

1979

1994

2004

2006

2016

Current release

V3, October 2020

4.4, October 2021

9.5.0, February 2024

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

free licenses can be provided

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

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

Android
Linux

ARM, x86
Raspbian
Windows

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

Linux
OS X
Unix
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

All OS with a Java 7 VM

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

RDF Schemas

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

ANSI SQL queries

A subset of ANSI SQL is implemented

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

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Java API
RESTful HTTP/JSON API

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

C
C++
Delphi
Java
Perl
PHP
Tcl

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

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

none

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Isolation support depends on the API used

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

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 user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

yes

Access rights for users and roles

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