DBMS > Faircom DB vs. Faircom EDGE vs. FeatureBase vs. H2 vs. Ingres

System Properties Comparison Faircom DB vs. Faircom EDGE vs. FeatureBase vs. H2 vs. Ingres

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

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

Well established RDBMS

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	0.26
Rank	#304	Overall
	#138	Relational DBMS

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	4.67
Rank	#77	Overall
	#42	Relational DBMS

Website

www.faircom.com/products/faircom-db

www.faircom.com/products/faircom-edge

www.featurebase.com

www.h2database.com

www.actian.com/databases/ingres

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

docs.featurebase.com

www.h2database.com/html/main.html

docs.actian.com/ingres

Developer

FairCom Corporation

Molecula and Pilosa Open Source Contributors

Thomas Mueller

Actian Corporation

Initial release

1979

2017

2005

1974

originally developed at University Berkely in early 1970s

Current release

V12, November 2020

V3, October 2020

2022, May 2022

2.2.220, July 2023

11.2, May 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Restricted, free version available

commercial

Open Source

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

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++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS

All OS with a Java VM

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Data scheme

schema free, schema optional, schema required, partial schema,

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

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes, ANSI Standard SQL Types

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

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

ANSI SQL queries

SQL queries

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

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

gRPC
JDBC
Kafka Connector
ODBC

JDBC
ODBC

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

Java
Python

Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

.Net, JavaScript, C/C++

Java Stored Procedures and User-Defined Functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

File partitioning

Customizable business rules for partitioning

Sharding

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

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

Ingres Replicator

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

yes

MVCC

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

yes, using Linux fsync

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 with additional protections for files

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

fine grained access rights according to SQL-standard

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