DBMS > Faircom EDGE vs. HBase vs. HyperSQL vs. IRONdb vs. RDF4J

System Properties Comparison Faircom EDGE vs. HBase vs. HyperSQL vs. IRONdb vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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.

Primary database model

Key-value store
Relational DBMS

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

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.faircom.com/products/faircom-edge

hbase.apache.org

hsqldb.org

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

rdf4j.org

Technical documentation

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

hbase.apache.org/book.html

hsqldb.org/web/hsqlDocsFrame.html

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

rdf4j.org/documentation

Developer

FairCom Corporation

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Circonus LLC.

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

Initial release

1979

2008

2001

2017

2004

Current release

V3, October 2020

2.3.4, January 2021

2.7.2, June 2023

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache version 2

Open Source

based on BSD license

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

ANSI C, C++

Java

C and C++

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux

Linux
OS X
Unix
Windows

Data scheme

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

schema-free, schema definition possible

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

options to bring your own types, AVRO

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

yes

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

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

HTTP API

JDBC via HTTP
JDBC
ODBC

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

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

C
C#
C++
Groovy
Java
PHP
Python
Scala

All languages supporting JDBC/ODBC
Java

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Coprocessors in Java

Java, SQL

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication
Source-replica replication

none

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

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

ACID

Isolation support depends on the API used

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

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

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