DBMS > DataFS vs. Firebird vs. ObjectBox vs. Stardog

System Properties Comparison DataFS vs. Firebird vs. ObjectBox vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Firebird is an open source RDBMS forked from Borland's InterBase

Lightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Object oriented DBMS

Relational DBMS

Object oriented DBMS
Vector DBMS

Graph DBMS
RDF store

Secondary database models

Graph DBMS

Time Series DBMS

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

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	19.46
Rank	#30	Overall
	#18	Relational DBMS

Score	1.08
Rank	#179	Overall
	#6	Object oriented DBMS
	#9	Vector DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

newdatabase.com

www.firebirdsql.org

github.com/objectbox
objectbox.io

www.stardog.com

Technical documentation

dev.mobiland.com/Overview.xsp

www.firebirdsql.org/en/reference-manuals

docs.objectbox.io

docs.stardog.com

Developer

Mobiland AG

Firebird Foundation

ObjectBox Limited

Stardog-Union

Initial release

2018

2000

As fork of Borland's InterBase

2017

2010

Current release

1.1.263, October 2022

5.0.1, August 2024

4.0 (May 2024)

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Initial Developer's Public License

Bindings are released under Apache 2.0

Apache License 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

C and C++

Java

Server operating systems

Windows

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Android
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows

Linux
macOS
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

yes, plus "flex" map-like types

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

Proprietary native API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.Net
C
C#
C++
VB.Net

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

C
C++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

PSQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

no, except callback-events from server when changes happened

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Data sync between devices allowing occasional connected databases to work completely offline

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

Durability

Support for making data persistent

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

Windows-Profile

Users with fine-grained authorization concept

yes

Access rights for users and roles

More information provided by the system vendor

News

MongoDB Realm & Device Sync alternatives – ObjectBox
17 September 2024

Local AI – what it is and why we need it
11 September 2024

First on-device Vector Database (aka Semantic Index) for iOS
24 July 2024

Retrieval Augmented Generation (RAG) with vector databases: Expanding AI Capabilities
18 June 2024

The on-device Vector Database for Android and Java
29 May 2024

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