DBMS > IRONdb vs. Linter vs. MarkLogic vs. OrigoDB vs. RDF4J

System Properties Comparison IRONdb vs. Linter vs. MarkLogic 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

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

RDBMS for high security requirements

Operational and transactional Enterprise NoSQL database

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

Time Series DBMS

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Object oriented DBMS

RDF store

Secondary database models

Spatial DBMS

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

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

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

Technical documentation

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

docs.marklogic.com

origodb.com/docs

rdf4j.org/documentation

Developer

Circonus LLC.

relex.ru

MarkLogic Corp.

Robert Friberg et al

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

Initial release

2017

1990

2001

2009

under the name LiveDB

2004

Current release

V0.10.20, January 2018

11.0, December 2022

License

Commercial or Open Source

commercial

restricted free version is available

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

C and C++

C++

Java

Server operating systems

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
OS X
Windows

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

schema-free

Schema can be enforced

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

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

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL92

APIs and other access methods

HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

.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

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

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net

Java
PHP
Python

Server-side scripts

Stored procedures

yes, in Lua

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

via XQuery or JavaScript

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

Source-replica replication

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

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, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

Role based authorization

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