DBMS > AgensGraph vs. Faircom EDGE vs. IRONdb vs. RDF4J vs. searchxml

System Properties Comparison AgensGraph vs. Faircom EDGE vs. IRONdb vs. RDF4J vs. searchxml

Editorial information provided by DB-Engines

Name

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

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

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.

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

RDF store

Native XML DBMS
Search engine

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

bitnine.net/agensgraph

www.faircom.com/products/faircom-edge

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

rdf4j.org

www.searchxml.net/category/products

Technical documentation

bitnine.net/documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

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

rdf4j.org/documentation

www.searchxml.net/support/handouts

Developer

Bitnine Global Inc.

FairCom Corporation

Circonus LLC.

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

informationpartners gmbh

Initial release

2016

1979

2017

2004

2015

Current release

2.1, December 2018

V3, October 2020

V0.10.20, January 2018

1.0

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Restricted, free version available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

ANSI C, C++

C and C++

Java

C++

Server operating systems

Linux
OS X
Windows

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
OS X
Unix
Windows

Windows

Data scheme

depending on used data model

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

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

APIs and other access methods

Cypher Query Language
JDBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

C
Java
JavaScript
Python

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

Java
PHP
Python

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in Lua

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

File partitioning

Customizable business rules for partitioning

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

configurable replication factor, datacenter aware

none

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Isolation support depends on the API used

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

fine grained access rights according to SQL-standard

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Domain, group and role-based access control at the document level and for application services

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