DBMS > FatDB vs. IRONdb vs. RDF4J vs. SwayDB

System Properties Comparison FatDB vs. IRONdb vs. RDF4J vs. SwayDB

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Key-value store

Time Series DBMS

RDF store

Key-value store

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

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Website

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

rdf4j.org

swaydb.simer.au

Technical documentation

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

rdf4j.org/documentation

Developer

FatCloud

Circonus LLC.

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

Simer Plaha

Initial release

2012

2017

2004

2018

Current release

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GNU Affero GPL V3.0

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++

Java

Scala

Server operating systems

Windows

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL

Support of SQL

Via inetgration in SQL Server

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

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP API

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

Java
PHP
Python

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

via applications

yes, in Lua

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Can implement custom security layer via applications

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More resources

Recent citations in the news

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21 February 2024, SiliconANGLE News

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