DBMS > Faircom EDGE vs. H2 vs. Hive vs. IRONdb vs. RDF4J

System Properties Comparison Faircom EDGE vs. H2 vs. Hive 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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Relational DBMS

Time Series DBMS

RDF store

Secondary database models

Spatial 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	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.faircom.com/products/faircom-edge

www.h2database.com

hive.apache.org

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

rdf4j.org

Technical documentation

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

www.h2database.com/html/main.html

cwiki.apache.org/confluence/display/Hive/Home

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

rdf4j.org/documentation

Developer

FairCom Corporation

Thomas Mueller

Apache Software Foundation

initially developed by Facebook

Circonus LLC.

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

Initial release

1979

2005

2012

2017

2004

Current release

V3, October 2020

2.2.220, July 2023

3.1.3, April 2022

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2

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

All OS with a Java VM

Linux

Linux
OS X
Unix
Windows

Data scheme

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

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

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

yes

SQL-like DML and DDL statements

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

JDBC
ODBC

JDBC
ODBC
Thrift

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

Java

C++
Java
PHP
Python

.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++

Java Stored Procedures and User-Defined Functions

yes

user defined functions and integration of map-reduce

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding

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

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual 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

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes, multi-version concurrency control (MVCC)

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.

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

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