DBMS > ArcadeDB vs. FileMaker vs. IRONdb vs. OrigoDB vs. RDF4J

System Properties Comparison ArcadeDB vs. FileMaker vs. IRONdb vs. OrigoDB vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A fully ACID in-memory object graph database

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

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

RDF store

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

Score	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

arcadedb.com

www.claris.com/filemaker

www.circonus.com/solutions/time-series-database/

origodb.com

rdf4j.org

Technical documentation

docs.arcadedb.com

www.claris.com/resources/documentation

docs.circonus.com/irondb/category/getting-started

origodb.com/docs

rdf4j.org/documentation

Developer

Arcade Data

Claris

a subsidiary of Apple

Circonus LLC.

Robert Friberg et al

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

Initial release

2021

1983

2017

2009

under the name LiveDB

2004

Current release

September 2021

19.4.1, November 2021

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

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

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM

iOS

client part only
Linux
OS X
Windows

Linux

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

User defined using .NET types and collections

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.

yes

A http query request can return the data in XML format

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language, no joins

yes

via plugins

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

Filemaker WebDirect
JDBC
ODBC

HTTP API

.NET Client API
HTTP API
LINQ

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Java

PHP

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net

Java
PHP
Python

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Source-replica replication, warm standby

since Version 14

configurable replication factor, datacenter aware

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

relationship in graphs

yes

depending on model

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

simple rights management via user accounts and connection to external directory services

Role based authorization

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