DBMS > ArcadeDB vs. CouchDB vs. FatDB vs. IRONdb

System Properties Comparison ArcadeDB vs. CouchDB vs. FatDB vs. IRONdb

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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

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

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Document store

Document store
Key-value store

Time Series DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

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	9.30
Rank	#45	Overall
	#7	Document stores

Website

arcadedb.com

couchdb.apache.org

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

Technical documentation

docs.arcadedb.com

docs.couchdb.org/en/stable

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

Developer

Arcade Data

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

FatCloud

Circonus LLC.

Initial release

2021

2005

2012

2017

Current release

September 2021

3.3.3, December 2023

V0.10.20, January 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

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

Erlang

C and C++

Server operating systems

All OS with a Java VM

Android
BSD
Linux
OS X
Solaris
Windows

Windows

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

via views

yes

SQL

Support of SQL

SQL-like query language, no joins

Via inetgration in SQL Server

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

RESTful HTTP/JSON API

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

HTTP API

Supported programming languages

Java

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

Server-side scripts

Stored procedures

View functions in JavaScript

yes

via applications

yes, in Lua

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

selectable replication factor

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

atomic operations within a single document possible

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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.

User concepts

Access control

Access rights for users can be defined per database

Can implement custom security layer via applications

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More resources

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