DBMS > Faircom DB vs. JaguarDB vs. NSDb vs. RDF4J

System Properties Comparison Faircom DB vs. JaguarDB vs. NSDb vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

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

Performant, highly scalable DBMS for AI and IoT applications

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

Primary database model

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

RDF store

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

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

Score	0.00
Rank	#385	Overall
	#61	Key-value stores
	#16	Vector DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

www.faircom.com/products/faircom-db

www.jaguardb.com

nsdb.io

rdf4j.org

Technical documentation

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

www.jaguardb.com/support.html

nsdb.io/Architecture

rdf4j.org/documentation

Developer

FairCom Corporation

DataJaguar, Inc.

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

Initial release

1979

2015

2017

2004

Current release

V13, July 2024

3.3 July 2023

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

GPL V3.0

Open Source

Apache Version 2.0

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++

C++

the server part. Clients available in other languages

Java, Scala

Java

Server operating systems

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

easily portable to other OSs

Linux

Linux
macOS

Linux
OS X
Unix
Windows

Data scheme

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

yes

RDF Schemas

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

APIs and other access methods

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

JDBC
ODBC

gRPC
HTTP REST
WebSocket

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++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Java
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

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).

Multi-source replication

none

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

Eventual 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

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

Using Apache Lucene

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

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

rights management via user accounts

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