DBMS > Faircom DB vs. Newts vs. OrientDB vs. Quasardb vs. Valentina Server

System Properties Comparison Faircom DB vs. Newts vs. OrientDB vs. Quasardb vs. Valentina Server

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.

Time Series DBMS based on Cassandra

Multi-model DBMS (Document, Graph, Key/Value)

Distributed, high-performance timeseries database

Object-relational database and reports server

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

www.faircom.com/products/faircom-db

opennms.github.io/newts

orientdb.org

quasar.ai

www.valentina-db.net

Technical documentation

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

github.com/OpenNMS/newts/wiki

www.orientdb.com/docs/last/index.html

doc.quasar.ai/master

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

FairCom Corporation

OpenNMS Group

OrientDB LTD; CallidusCloud; SAP

quasardb

Paradigma Software

Initial release

1979

2014

2010

2009

1999

Current release

V12, November 2020

3.2.29, March 2024

3.14.1, January 2024

5.7.5

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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

C++

Server operating systems

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

easily portable to other OSs

Linux
OS X
Windows

All OS with a Java JDK (>= JDK 6)

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

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

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language, no joins

SQL-like query language

yes

APIs and other access methods

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

HTTP REST
Java API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

HTTP API

ODBC

Supported programming languages

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

Java

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Java, Javascript

yes

Triggers

yes

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

based on Cassandra

Sharding

consistent hashing

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).

selectable replication factor

based on Cassandra

Multi-source replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

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

Access rights for users and roles; record level security configurable

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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