DBMS > Atos Standard Common Repository vs. Dolt vs. Faircom DB vs. RDF4J vs. Sequoiadb

System Properties Comparison Atos Standard Common Repository vs. Dolt vs. Faircom DB vs. RDF4J vs. Sequoiadb

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A MySQL compatible DBMS with Git-like versioning of data and schema

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

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

NewSQL database with distributed OLTP and SQL

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store
Relational DBMS

RDF store

Document store
Relational DBMS

Secondary database models

Document store

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

Score	0.93
Rank	#194	Overall
	#90	Relational DBMS

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

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

github.com/dolthub/dolt
www.dolthub.com

www.faircom.com/products/faircom-db

rdf4j.org

www.sequoiadb.com

Technical documentation

docs.dolthub.com

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

rdf4j.org/documentation

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Atos Convergence Creators

DoltHub Inc

FairCom Corporation

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

Sequoiadb Ltd.

Initial release

2016

2018

1979

2004

2013

Current release

1703

V13, July 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Server: AGPL; Client: Apache V2

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

Java

ANSI C, C++

Java

C++

Server operating systems

Linux

Linux
macOS
Windows

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

easily portable to other OSs

Linux
OS X
Unix
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

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

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

optional

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

yes

yes, ANSI SQL with proprietary extensions

SQL-like query language

APIs and other access methods

LDAP

CLI Client
HTTP REST

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

proprietary protocol using JSON

Supported programming languages

All languages with LDAP bindings

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Java
PHP
Python

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

.Net, JavaScript, C/C++

yes

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of specific operations

ACID

tunable from ACID to Eventually Consistent

ACID

Isolation support depends on the API used

Document is locked during a transaction

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

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

LDAP bind authentication

Only one user is configurable, and must be specified in the config file at startup

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

simple password-based access control

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More resources

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